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      <title>Ohio Advances Nation’s First Statewide Drone First Responder Program with Selection of Nine Public Safety Agencies</title>
      <link>https://www.calanalytics.com/ohio-statewide-drone-for-first-responders-program</link>
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          COLUMBUS, OH (February 9, 2026)
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         — Ohio continues to lead the nation in modernizing emergency response with the selection of nine public safety agencies to participate in the
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          Ohio Statewide Drone First Responder (DFR) Pilot Program
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         , a first-of-its-kind initiative designed to expand rapid aerial response capabilities across communities of all sizes.
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          The program, led by the Ohio Department of Transportation (ODOT) and DriveOhio, with program management support from SkyfireAI, reflects Ohio’s commitment to equipping first responders with innovative tools that improve situational awareness, enhance officer and responder safety, and reduce emergency response times.
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          The following agencies have been selected to participate in the pilot program:
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            City of Springfield Police/Fire/EMS
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            Athens Police Department
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            Lima Police Department
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            Toledo Police Department
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            Violet Township Fire/EMS
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            Austintown Fire Department
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            City of Hamilton Police/Fire/EMS
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            Amherst Police Department
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            Kelleys Island Fire/EMS
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          Together, these agencies represent a diverse cross-section of Ohio, spanning urban centers, suburban communities, and rural jurisdictions — reinforcing the program’s goal of ensuring access to advanced emergency response capabilities statewide. Many of the selected agencies have also committed to sharing drone resources with neighboring jurisdictions, further expanding the impact of this program.
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          “Ohio is committed to giving our first responders the tools they need to protect lives and serve their communities safely and effectively,” said
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           Governor Mike DeWine
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          . “This statewide Drone First Responder pilot reflects Ohio’s leadership in innovation, our strong partnership with local agencies, and our focus on using technology responsibly to support public safety across the state.”
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          Created under Ohio House Bill 96, the Ohio Statewide DFR Pilot Program enables participating agencies to deploy state-approved, NDAA-compliant drone systems capable of rapid launch, real-time video streaming to command staff, and integration into Ohio’s emerging uncrewed aircraft traffic management framework, led by Ohio-based CAL Analytics.
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          “We are enthusiastic supporters of DFR program and thankful for the efforts of so many Ohio leaders to accelerate this program into the execution phase,” said
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           State Representative Adam Holmes
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          , who championed the program's inclusion in the bill. “The DFR program will greatly enhance first responder effectiveness and will provide increased support for all Ohioans.   A tremendous capability for our priceless first responders.”
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          “Ohio is leading the way for the nation with the first statewide DFR program! Our ODOT leaders are setting the stage for an integrated network of DFR assets that will undoubtedly save lives and resources and be a force-multiplier for our undermanned first responder organizations,” said
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           State Representative Bernard Willis
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          , Chair of the House Transportation Committee. “In Ohio, the HOME of aviation we are the BEST at making historic innovations with flying machines, and we will NEVER stop leading the way!”
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          With technology known as drone-in-a-box, a drone can be launched from a permanent docking site and be flown remotely to a scene. These pre-positioned drones provide real-time visual information to aid decision-making, and some can also drop critical medical supplies to the scene of an emergency. The program emphasizes operational readiness, standardized training, and responsible use, while maintaining a strong focus on community engagement and transparency.
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          “This program is about thoughtful implementation, not experimentation,” said
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           Richard Fox, Director of the DriveOhio UAS Center
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          . "By coordinating procurement, training, and regulatory support at the state level, Ohio is creating a scalable model that benfits communities of every size. We're grateful for the confidence the legislature has in ODOT to execute this groundbreaking program." 
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          SkyfireAI, a national leader in public safety drone program development and FAA regulatory integration, is supporting the initiative as program manager, working closely with ODOT, DriveOhio, and participating agencies to ensure consistent standards and successful deployment.
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          “The selection of these nine agencies reflects the strength of Ohio’s public safety community and the seriousness with which this program has been approached,” said
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           Don Mathis, Co-Founder and CEO SkyfireAI
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          . "This pilot lays the foundation for how states can responsibly scale DFR programs today — and prepare for future capabilities as technology continues to evolve." 
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          Selected agencies will participate in a structured onboarding process beginning in early 2026, including hands-on vendor demonstrations, training, and program coordination. Operations are expected to begin in Spring 2026 and continue for approximately a year. The pilot program will evaluate operational effectiveness, response outcomes, and opportunities for future expansion.
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          Ohio’s Statewide Drone First Responder Pilot Program has already drawn national attention as a potential blueprint for other states seeking to modernize emergency response while maintaining strong governance, safety, and public trust.
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          ###
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           About CAL Analytics:
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          CAL Analytics is a small business focusing on the development of aviation and autonomous systems. Located in Dayton, OH and founded in 2010, CAL has expertise in navigation systems, remote sensing, signal analysis, and information fusion. CAL is a leader in UAS Traffic Management (UTM) and Advanced Air Mobility (AAM) technology, offering airspace management, mission management, detect and avoid, and in-time system-wide safety assurance (ISSA) solutions. Our mission is to provide agile and rigorous approaches to bring new technologies to the world. More information at
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           About SkyfireAI:
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          SkyfireAI is an AI-native drone technology company redefining mission-scale autonomy for public safety, defense, and enterprise markets. Our cloud-connected and edge-enabled software platform powers autonomous swarming, Beyond Visual Line of Sight (BVLOS) operations, and real-time AI analytics—unlocking faster response, smarter decision-making, and safer operations. Built for scalability, SkyfireAI’s solutions deliver repeatable, high-value outcomes across thousands of missions, positioning the company to capitalize on a rapidly expanding global uncrewed systems market projected to exceed $100 billion by 2030. More information is available at
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          .
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           About ODOT’s DriveOhio Initiative:
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          DriveOhio is the state’s center for smart mobility, advancing connected, automated, shared, and electric transportation. The UAS Center, located in Springfield, OH, is the statewide resource for uncrewed aircraft systems testing, integration, and operations.
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      <pubDate>Tue, 10 Feb 2026 16:11:43 GMT</pubDate>
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      <title>CAL and AV Launch Operational BVLOS Airspace Management Facility in Partnership with the U.S. Air Force and Ohio Department of Transportation</title>
      <link>https://www.calanalytics.com/cal-and-av-launch-operational-bvlos-facility</link>
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         CAL AAM Enterprise Platform
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           SPRINGFIELD, Ohio
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           – January 27, 2026
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            – CAL Analytics, an innovator in advanced airspace management technologies, and AeroVironment, Inc. (“AV”) (NASDAQ: AVAV), a global defense technology leader delivering software-enabled disruptive autonomous systems, today announced the completed installation and initial operation of a new Beyond Visual Line of Sight (BVLOS) airspace management facility at the National Advanced Air Mobility Center of Excellence (NAAMCE) at Springfield-Beckley Municipal Airport in Springfield, Ohio. 
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          Initially developed under a Cooperative Research and Development Agreement (CRADA) between the Air Force Research Laboratory (AFRL) and the Ohio Department of Transportation (ODOT), the project now features an upgraded installation that integrates AV_Halo™ COMMAND, AV’s command and control (C2) architecture, with CAL Analytics’ Advanced Air Mobility (AAM) enterprise platform to establish the nation’s premiere test environment and management facility, where Department of War operators can safely conduct BVLOS missions in shared airspace utilizing existing Federal Aviation Administration (FAA) ground radar feeds. 
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          “This installation establishes the blueprint for how airports and states across the country can safely integrate uncrewed aircraft into existing airspace,” said Wahid Nawabi, Chairman, President and Chief Executive Officer at AV. “As the FAA defines the future of BVLOS rule-making, this facility provides the real-world operational data, safety validation, and interoperability framework regulators need. The system we’ve installed in Ohio isn’t just a mock-up or a test site — pending FAA approval it will be an operational and scalable model for nationwide deployment and the foundation for truly integrated air mobility.” 
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          The integration will support flight tests, evaluation, and day-to-day operations by routing AFRL’s access to the FAA’s ground-radar network through AV_Halo™ COMMAND — AV's modular, software-driven C2 architecture that fuses multiple enhanced sensor feeds, into a single, secure operating picture, giving operators continuous situational awareness for BVLOS mission planning and airspace safety. 
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          “AV_Halo is the connective tissue that turns a collection of sensors, radars, and platforms into a living, breathing airspace system,” said Stephen Lloyd, Senior Director C2, CUAS, and Tracking at AV. “By fusing FAA ground radar, and COTS surveillance sensors into a single, secure operating picture, AV_Halo delivers the assured visibility and machine-speed decision support needed for predictable BVLOS operations—and makes it possible to scale effortlessly from a single site to an entire statewide corridor.” 
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          When combined with CAL Analytics’ AAM enterprise platform, the system unifies radar and advanced DAA into a single real-time airspace view—enabling detect-and-avoid, extending autonomous BVLOS operations with precision and confidence.  
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          “Beyond Visual Line of Sight operations are the key to unlocking the next generation of air mobility,” said Dr. Sean Calhoun, Managing Director of CAL Analytics. “This facility will prove that BVLOS can be executed safely and reliably in shared airspace—and that matters because it sets the foundation for statewide corridors, national standards, and an entirely new layer of transportation infrastructure that will reshape how we move people, goods, data, and critical services across the country.” 
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          Pending full FAA approval, the facility will support local missions and real-time monitoring of UAS activity as AFRL, ODOT, the FAA, AV, and CAL Analytics collaborate to validate airspace-safety technologies, advance air-mobility corridors, drive economic development, and shape national BVLOS rules and integration standards. 
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          Plans are already underway to extend the system to enable corridors between Springfield and Columbus, Ohio, adding new radar sites and expanding detect-and-avoid coverage to support broader BVLOS operations across Ohio and additional sites nationwide. 
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      <pubDate>Wed, 28 Jan 2026 21:49:59 GMT</pubDate>
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      <title>CAL Analytics Partners with AirData to Provide LAANC to Drone Operators</title>
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          Partnership to Streamline Real-Time Airspace Authorizations and Improve Airspace Awareness
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         (DAYTON, Ohio) – CAL Analytics is pleased to announce a strategic partnership with AirData UAV to integrate the Low Altitude Authorization and Notification Capability (LAANC) into AirData’s suite of drone operation services. This collaboration streamlines operations and creates safety awareness for drone pilots by providing real-time airspace data and automated authorizations for flight in controlled airspace.
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          AirData is committed to delivering an intuitive customer experience, with CAL’s LAANC service fully integrating into its existing fleet management platform. All AirData users can request LAANC authorizations through both the AirData web portal and the AirData mobile app.
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          “This partnership with AirData marks a significant milestone for CAL Analytics,” said Dr. Sean Calhoun, Founder and Managing Director of CAL Analytics. “By integrating the LAANC capability into AirData’s already extensive software platform, we are able to offer AirData users a seamless LAANC authorization experience, enhancing both safety and efficiency in drone operations.”
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          CAL is an FAA-approved UAS Service Supplier (USS) of LAANC, a collaborative effort between the Federal Aviation Administration (FAA) and industry partners, providing drone pilots with access to controlled airspace at or below 400 feet. This partnership with CAL will allow AirData users to quickly and efficiently obtain necessary flight authorizations, ensuring compliance and improving operational efficiency.
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          “Providing intuitive and straightforward workflows for our customers is of paramount importance to us,” said AirData CEO Eran Steiner. “We are thrilled to partner with CAL Analytics to deliver best-in-class LAANC services. AirData is dedicated to offering our customers seamless and comprehensive drone fleet management, encompassing everything from mission planning and pilot certifications to compliance and flight data analysis.
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          The LAANC integration for 3rd parties like AirData is another key component of CAL Analytics’ suite of UAS airspace management services, providing critical real-time data and automated processes that support safe and efficient drone operations. This partnership underscores CAL Analytics’ commitment to advancing the capabilities of UAS and promoting safe airspace practices.
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          AirData’s collaboration with CAL will provide a streamlined LAANC workflow for users, allowing for quick and straightforward access from a variety of devices and locations. LAANC requests can be tied into AirData’s Mission Planning tool set and are recorded in the user’s account for compliance and record-keeping. The AirData platform provides a extensive set of features that benefit the entirety of the drone flight workflow, from pre-flight checklists to live video streaming during flights, with sophisticated post-flight fleet data analytics to maximize the value and safety of drone operations. This unified fleet management approach allows customers to mitigate risk, improve fleet efficiency, and manage end-to-end compliance.
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           About CAL Analytics
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          CAL Analytics is focused on the development of aviation and autonomous systems. Located in Dayton, OH and founded in 2010, CAL has expertise in navigation systems, remote sensing, signal analysis, and information fusion. CAL is a leader in UAS Traffic Management (UTM) and Advanced Air Mobility (AAM) technology, offering airspace management, mission management, detect and avoid, and in-time system-wide safety assurance (ISSA) solutions. Our mission is to provide agile and rigorous approaches to bring new technologies to the world.
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          To learn more about CAL Analytics, please visit
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            About AirData
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          AirData is the largest online drone fleet data management and real-time flight streaming platform, serving over 330,000 users with 42 million flights uploaded to date, capturing an average of 25,000 high-resolution flight records a day. AirData is used by large fleet operators around the world as a comprehensive flight safety data analysis and crash prevention platform, with advanced maintenance, mission planning, pilot tracking, and easy-to-use live streaming.
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          To learn more about AirData, please visit
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           https://airdata.com/ 
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      <pubDate>Mon, 04 Nov 2024 13:38:22 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/cal-airdata-laanc</guid>
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      <title>CAL Analytics Awarded AFWERX SBIR Direct-to-Phase II Contract</title>
      <link>https://www.calanalytics.com/cal-analytics-awarded-afwerx-sbir-direct-to-phase-ii-contract</link>
      <description />
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         Partnering with BlueHalo to Enhance Airspace Management Services for the 
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          Department of the Air Force
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          Dayton, Ohio
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         –CAL Analytics announces it has been selected by AFWERX for a SBIR Direct-to-Phase II contract focused on building out, integrating, and testing an airspace surveillance and deconfliction technology for crewed and uncrewed aircraft of all sizes. This capability is critical to providing air traffic services for seamlessly integrating new aircraft types into the airspace for both permanent and tactical environments. In partnership with BlueHalo, the company transforming the future of global defense with market-leading solutions for command and control (C2) and airspace protection, CAL Analytics will use a combination of local and regional airspace surveillance feeds to verify the performance of the airspace surveillance tracking and deconfliction services. 
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          “Airspace surveillance tracking systems play an important role in Advanced Air Mobility (AAM) operations and are essential for providing DAA/deconfliction services to remotely piloted aircraft across many classes of airspace,” says Dr. Sean Calhoun of CAL Analytics. “These tracking systems ingest the various airspace surveillance sensors (ground radars, transponders, ADS-B, optical, etc.) and are required to correlate all the data from these sensors and provide a clean/fused airspace picture. To date, there is no tracking system in operational use that performs this function with the necessary plug-and-play capability required to support the true operationalization and scaling of AAM capabilities.” 
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          The Air Force Research Laboratory and AFWERX have partnered to streamline the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) process by accelerating the small business experience through faster proposal to award timelines, changing the pool of potential applicants by expanding opportunities to small business and eliminating bureaucratic overhead by continually implementing process improvement changes in contract execution. The DAF began offering the Open Topic SBIR/STTR program in 2018 which expanded the range of innovations the DAF funded and now on June 12th, 2024, CAL Analytics will continue its journey to create and provide innovative capabilities that will strengthen the national defense of the United States of America.
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          “The views expressed are those of the author and do not necessarily reflect the official policy or position of the Department of the Air Force, the Department of Defense, or the U.S. government.”
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           About CAL Analytics
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          CAL Analytics is a small business focusing on the development of aviation and autonomous systems. Located in Dayton, OH and founded in 2010, CAL has expertise in navigation systems, remote sensing, signal analysis, and information fusion. CAL is a leader in UAS Traffic Management (UTM) and Advanced Air Mobility (AAM) technology, offering airspace management, mission management, detect and avoid, and in-time system-wide safety assurance (ISSA) solutions. Our mission is to provide agile and rigorous approaches to bring new technologies to the world.
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          BlueHalo is purpose-built to provide industry-leading capabilities in the areas of Space, C-UAS and Autonomous Systems, Electronic Warfare &amp;amp; Cyber, and AI/ML. The company develops and brings to market next-generation capabilities to support customers’ critical missions and national security. Learn more at
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           https://www.bluehalo.com
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          and follow BlueHalo on
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           LinkedIn
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          The Air Force Research Laboratory is the primary scientific research and development center for the Department of the Air Force. AFRL plays an integral role in leading the discovery, development, and integration of affordable warfighting technologies for our air, space, and cyberspace force. With a workforce of more than 12,500 across nine technology areas and 40 other operations across the globe, AFRL provides a diverse portfolio of science and technology ranging from fundamental to advanced research and technology development. For more information, visit afresearchlab.com.
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          As the innovation arm of the DAF and a directorate within the Air Force Research Laboratory, AFWERX brings cutting-edge American ingenuity from small businesses and start-ups to address the most pressing challenges of the DAF. AFWERX employs approximately 370 military, civilian and contractor personnel at five hubs and sites executing an annual $1.4 billion budget. Since 2019, AFWERX has executed over 6,200 new contracts worth more than $4.7 billion to strengthen the U.S. defense industrial base and drive faster technology transition to operational capability. For more information, visit: www.afwerx.com.
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          Company Press Contact:
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          Sean Calhoun, PhD  
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          Founder and Managing Director  
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          info@calanalytics.com 
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      <pubDate>Wed, 11 Sep 2024 14:01:45 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/cal-analytics-awarded-afwerx-sbir-direct-to-phase-ii-contract</guid>
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      <title>Ohio Launches Traffic Management System for Drone Operations</title>
      <link>https://www.calanalytics.com/ohio-launches-utm-dss</link>
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         System to enhance safety for low altitude drone flights
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          Columbus, OH
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         – CAL Analytics, in coordination with the Ohio Department of Transportation (ODOT), has launched a low-altitude air traffic management system for drones to support statewide operations. As the number of uncrewed aircraft systems (UAS), or drones, grows, a robust system for managing the low-altitude airspace where these aircraft operate is necessary to ensure safety. While the Federal Aviation Administration (FAA) provides air traffic control for traditional aircraft flying in certain airspaces, low-altitude traffic management for drones is the responsibility of individual operators. Currently, drone pilots are required to keep the aircraft within sight to avoid a collision. A UAS Traffic Management (UTM) system enhances safety by enabling sharing of flight details between UAS operators, providing a digital tool for flight planning, and allowing operators to eventually operate beyond visual line of sight (BVLOS) while continuing to minimize the risk of collision. 
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          “The introduction of this vital capability continues Ohio’s tradition of innovation in the aviation community while prioritizing safety,” said Rich Fox, director of the Ohio UAS Center at ODOT. “As we collaborate with others at the newly opened National Advanced Air Mobility Center of Excellence, we expect this to be the first of many industry-leading activities coming out of that state-of-the-art facility.”
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          Following several state sponsored research efforts to determine the best way to develop and deploy traffic management for uncrewed aircraft in Ohio, this system, implemented by CAL Analytics, provides interoperability where any user can enroll to share and receive flight information. As drone technology continues to advance, traffic management will be a key enabler of BVLOS operations, which currently require special permission from the FAA once stringent safety requirements are met.
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          “We couldn’t be more thrilled to continue our collaboration with ODOT by deploying this discovery and synchronization services to fully realize this first of a kind operational UTM capability throughout the state of Ohio,” said Dr. Sean Calhoun, managing director of CAL Analytics. “This realization is the result of a lot of industry development, including the essential work from The Ohio State University research team and sponsored research from the Ohio Federal Research Network (OFRN). We are looking forward to working with the various interested stakeholders throughout the state and the FAA to learn from this system and to start scaling UAS operations throughout Ohio.”
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          ODOT and the City of Hilliard will be the first organizations to enroll in the system and begin exchanging information as they look to leverage UAS as a tool for everything from inspection and traffic monitoring to onsite situational awareness for first responders, such as police and fire department dispatches. 
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          “Hilliard is excited to leverage this and other airspace services that Ohio has established to enable our first responder drone operations” says Deputy Police Chief for Hilliard, Ron Clark.  “These services will be critical for us to achieve FAA approval and operate our drones in a safe and effective manner.”
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          In the coming years, more advanced and BVLOS drone operations will increase in Ohio, which means multiple operators may be flying in the same area to deliver medical supplies, perform emergency services, conduct infrastructure inspections, and even deliver commercial packages. For safe and successful scaling of commercial drone operations, it’s imperative that pilots have situational awareness for strategic deconfliction. 
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          While both private and public organizations can enroll in the traffic management system, this resource is particularly valuable for other state agencies and local governments across Ohio. These services are available at no cost to any operator or fleet manager that requests access and goes through the onboarding process. To learn more or request access, please contact CAL Analytics at info@calanalytics.com. 
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      <pubDate>Tue, 26 Mar 2024 21:31:29 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/ohio-launches-utm-dss</guid>
      <g-custom:tags type="string">UAS,AAM,ODOT,UTM,Drones,CAL</g-custom:tags>
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      <title>CAL Analytics to provide statewide UTM/UAS services to Ohio</title>
      <link>https://www.calanalytics.com/ohio-statewide-utm</link>
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         ODOT to leverage CAL’s UTM platform for statewide operations
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           Columbus, OH – CAL Analytics has been selected by the Ohio Department of Transportation (ODOT) to provide statewide Uncrewed Aircraft System (UAS) operation services using CAL’s UAS Service Supplier (USS) platform. This agreement is the culmination of a multi-year build-up of CAL’s UTM service platform that started in 2019 with a $1.4M award from the Ohio Federal Research Network (OFRN) to develop an interoperable and resilient contingency management system for Ohio UAS Operations. Through this work, Ohio continues its leadership in the innovation, research, development and utilization of UAS technology. CAL’s USS will provide ODOT a wide array of services, including a centralized monitoring and management capability of statewide infrastructure, such as communications, navigation and airspace surveillance equipment, critical for UAS Beyond Visual Line-of-Sight operations.  Additionally, CAL will provide ODOT with enhanced operational planning and situational awareness for its extensive statewide utilization of UAS for Visual Line-of-Sight operations. 
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           “Ohio and ODOT in particular, has been on the forefront of embracing UAS technology, so we are very excited to have our USS platform provide the basis for statewide utilization,” said Dr. Sean Calhoun, Managing Director of CAL Analytics. “We have put a lot of our system development focus making sure our platform provides a host of performance and safety related features. Our work with NASA and integrating our health and integrity monitoring capabilities into our deployments will ensure statewide systems can scale in a robust and safe way.”
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           “CAL Analytics technology will help us take support of our uncrewed aircraft operations to the next level. Not only will our remote pilots use it for situational awareness and safety, but we are exploring the ability to expand this service to first responders across the state to better coordinate air support during an emergency,” said Rich Fox, UAS Director – Ohio UAS Center for ODOT.
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           This agreement between CAL Analytics and the Ohio Department of Transportation is big win for the State of Ohio and the state of UAS ecosystem growth. Ohio is a leader in the Advanced Air Mobility business development aspect of UAS operations and the individuals involved in the OFRN are proud to have played a part in supporting new technology and innovation development,” said Maj Gen (Ret.) Mark Bartman, OFRN Program Executive for Parallax Advanced Research. 
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      <pubDate>Wed, 03 May 2023 00:06:07 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/ohio-statewide-utm</guid>
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      <title>In-time System-wide Safety Assurance (ISSA) System Deployed to Ohio UTM</title>
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         CAL Analytics Deploys ISSA System to Ohio under NASA Program
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           Columbus, OH – CAL Analytics has been awarded a NASA Phase II-E Small Business Innovation Research (SBIR) award to deploy their Health &amp;amp; Integrity System (HIMS) to the Ohio Department of Transportation’s Uncrewed Traffic Management (UTM) system. This will be the first-time an In-time System-wide Safety Assurance (ISSA) system will be deployed and integrated into a functioning UTM environment for operations in an urban environment.   
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           This initiative is researching ways in which the resiliency and robustness of UTM ecosystems can and should be improved. The primary result of those activities was the formulation of a flexible, service-based architecture for Health &amp;amp; Integrity (H&amp;amp;I) monitoring, assessment, and mitigation of complex, federated System of Systems (SoS). This aptly named Health &amp;amp; Integrity Management System (HIMS) adds another dimension of capability to the UTM architecture wherein it is intended to holistically monitor and respond to the ecosystem, providing continuity between independent UTM services from a system reliability perspective
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           “Being able to evaluate our ISSA implementation in an operational environment that Ohio offers will be a critical step for validating the our various HIMS safety monitors and system interactions what will be key to ensuring a robust UTM ecosystem for safe low-altitude operations,” said Dr. Sean Calhoun, Managing Director of CAL Analytics. “Our HIMS system not only provides various real-time monitoring of key systems, such as surveillance and navigation, but we also provide capabilities assessing the impacts to operations and how to relay that information to operators.”
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           The CAL HIMS system builds off the Resilienx, Inc. FRAIHMWORK platform to realize a scalable ISSA system tailored specifically to UTM applications. The open architecture approach to the ISSA system enables seamless integration of future system monitoring and scalability.  
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           The effort builds off Ohio and NASA’s existing AAM National Campaign partnership, which includes System-Wide Safety, and The Ohio State Universities UTM development effort sponsored by the Ohio Department of Transportation (ODOT).  
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           “Safety is the number one goal at the Ohio Department of Transportation, operational assurance is the most important component in any aviation operation. As we continue to move towards highly automated and remote operations in the airspace, the health of systems and sensors providing information becomes crucial to maintain the safety for transportation on the ground and in the air,” said Fred Judson, UAS Director – Ohio UAS Center for ODOT.
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           “Safety is the key to innovation in aviation. Learning how and when to automate our safety monitoring, assessment, and mitigation functions enables us to design air systems that benefit all of us.” said Dr. Misty Davies, NASA’s Project Manager for System-Wide Safety Project.
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      <pubDate>Fri, 09 Sep 2022 13:25:20 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/in-time-system-wide-safety-assurance-issa-system-deployed-to-ohio-utm</guid>
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      <title>NUAIR Partners with CAL Analytics and FAA to Develop Methods for Certifying Detect and Avoid Services for Drone Operations</title>
      <link>https://www.calanalytics.com/nuair-partners-with-cal-analytics-and-faa-to-develop-methods-for-certifying-detect-and-avoid-services-for-drone-operations</link>
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         Upgrades to New York’s 50-mile Drone Corridor helps FAA and industry unlock safe, beyond visual line of sight drone operations
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          (SYRACUSE, NEW YORK)
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         – CAL Analytics is deploying their detect and avoid (DAA) service and contingency management platform (CMP) within New York’s 50-mile Drone Corridor and Federal Aviation Administration (FAA) designated uncrewed aircraft systems (UAS) Test Site at Griffiss International Airport, managed by NUAIR. The organizations are working jointly on a Technical Assistance program with the FAA to enable and approve a DAA service for low-altitude beyond visual line of sight (BVLOS) UAS operations.
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           Current regulations require drone operators to always have a pair of human eyes visually monitoring the airspace in which the drone is flying, limiting the ability to fly long distances. Without being able to see the drone, the system needs the ability to detect and avoid obstacles in the air and on ground to assure the safety of both crewed and uncrewed aircraft. The ability to safely fly BVLOS is the key to unlocking the full potential and economic advantage of routine commercial drone operations like medical and package deliveries.
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           “This upgrade to the 50-mile Drone Corridor between Rome and Syracuse will help integrate detect and avoid technology to further the safe advancement of unmanned aircraft systems into the national airspace and unlock the true potential of commercial drone operations,” said Oneida County Executive Anthony J. Picente Jr. “We look forward to the fruit this partnership between CAL Analytics and the FAA will bear at Oneida County’s Test Site, which continues to flourish under NUAIR’s management.”
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           The DAA service will be CAL’s newest UAS traffic management (UTM) service being brought to market and builds off their existing contingency management platform (CMP), which provides a suite of UTM services. CAL’s CMP will offer critical airspace services, including situational awareness from Kongsberg, conflict detection from CAL, health monitoring by ResilienX, and various weather services from TruWeather Solutions to help ensure the safe operation of uncrewed aircraft.
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           “We’re excited to partner with NUAIR and working directly with the FAA to get our DAA service approved for small UAS,” said Dr. Sean Calhoun, managing director of CAL Analytics. “This will go a long way towards validating several of the DAA standards, including those from ASTM-F38 and RTCA SC-147, and opening the skies for safe BVLOS drone operations.”
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           The BVLOS flight operations will be performed by NUAIR who manages operations of the New York UAS Test Site at Griffiss International Airport in Rome, NY, one of just seven FAA-designated UAS test sites in the United States. The CAL technology will provide several key services to enabling safe BVLOS operations within New York’s 50-mile UAS Corridor instrumented with radar, communication networks, including 5G, and other leading-edge technologies that facilitate advanced drone operations. 
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           “One of the most important recommendations from the recent BVLOS Aviation Rulemaking Committee was for the FAA to develop a methodology for approving safety-critical UTM services for BVLOS,” said NUAIR Chief Technology Officer Andy Thurling. “NUAIR is working with our partners at CAL Analytics and the FAA to do just that.”
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           NUAIR and its alliance of partners, including CAL Analytics, ResilienX, TruWeather Solutions and more will be showcasing their technologies and latest advancements at the upcoming AUVSI XPONENTIAL conference in Orlando, Florida, April 25-28. The companies are part of the GENIUS NY booth #1761, highlighting leading-edge companies in Central New York and taking applications for the next round of the $3 million business acceleration competition that is GENIUS NY.
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           ###
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           About NUAIR
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           NUAIR (Northeast UAS Airspace Integration Research Alliance, Inc.) is a New York-based nonprofit with a mission to safely integrate uncrewed aircraft systems (UAS) into the national airspace, enabling scalable, economically viable commercial drone operations. NUAIR manages operations of the FAA-designated New York UAS Test Site at Griffiss International Airport, Rome, NY on behalf of Oneida County and is responsible for the advancement of New York’s 50-mile UAS Corridor between Rome and Syracuse, NY. https://nuair.org
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      <pubDate>Thu, 21 Apr 2022 13:34:07 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/nuair-partners-with-cal-analytics-and-faa-to-develop-methods-for-certifying-detect-and-avoid-services-for-drone-operations</guid>
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      <title>FlyOhio to Partner with NASA in Deployment of Ohio's Advanced Air Mobility Ecosystem</title>
      <link>https://www.calanalytics.com/nasa-to-help-local-governments-plan-for-advanced-air-mobility</link>
      <description>CAL Analytics Part of Team Developing Framework for Statewide Strategy</description>
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         CAL Analytics Part of Team Developing Framework for Statewide Strategy
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         COLUMBUS - FlyOhio, a collaboration of public, private and academic institutions led by DriveOhio’s advanced air mobility (AAM) group, has been selected to participate in the National Aeronautics and Space Administration (NASA)’s
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          Advanced Air Mobility National Campaign
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         . The award focuses on system development through integrated vehicle and airspace demonstrations in real-world scenarios that are critical to safe and effective commercialization.
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          “In Ohio, we have a longstanding history with advancing aerospace technology, and we continue to pursue new opportunities to support the development of cutting-edge technology,” said Governor DeWine. “We are eager to join with NASA in a new, broad coalition of institutions across the state who are investing in the advancement of this transformative aerospace technology.”
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          Building upon Ohio’s ground-breaking work in “beyond-line-of-sight” drone systems and
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           SkyVision
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          work at Springfield-Beckley Airport and leveraging relationships established through the U.S. Air Force’s Agility Prime program, FlyOhio’s project will incorporate multiple use cases for personal travel and delivery of goods across the State of Ohio. The FlyOhio team is comprised of key aircraft manufacturers, operators, and airspace service providers and suppliers, as well as Ohio regional and city planning organizations, local stakeholders, academic institutions, and health care networks.
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          The multiyear program lays out an innovative framework to design, test and deploy a statewide AAM strategy focused on the movement of people and goods. Specifically, the winning proposal outlines test applications in health care delivery, air taxi or air metro, and regional air cargo transport.
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          The program’s development will be supported by the FlyOhio AAM Economic Impact Study due to be released June 6, 2021. A unique research effort to forecast the potential economic impacts of urban and regional air mobility, the report provides data on Ohio’s major urban centers and air corridors connecting its largest cities to rural communities. Also, the report examines Ohio’s advanced aviation infrastructure needs, revenue potential, and prospective job growth, among other economic indicators.
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          “Advanced Air Mobility technology is revolutionizing the transportation industry and Ohio is well positioned to lead market adoption as these solutions scale. At DriveOhio, we are committed to developing and deploying connected, automated, shared, and electric vehicles and infrastructure on the ground and in the air. This program is a manifestation of that ethos, and we are excited to increase the advanced aviation investment in Ohio’s economy,” said Howard Wood, executive director at DriveOhio. 
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          Home to more than 550 aerospace companies and three of the nation’s premier aerospace centers— NASA Glenn Research Center, NASA Plum Brook Station and Air Force Research Laboratory (AFRL) at Wright Patterson Air Force Base—Ohio is the nation’s largest aerospace industry supplier, with a workforce of more than 38,000 in the aviation and aerospace industry.
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          “JobsOhio is focused on accelerating Ohio’s economic growth and our partnership with FlyOhio and NASA will help to fulfill this mission,” said J.P. Nauseef, JobsOhio president and CEO. “Ohio’s economy has strong momentum as it emerges from the pandemic, and this powerful industry coalition is more evidence that Ohio is becoming the go-to spot in the Midwest for some of America’s top innovators. This campaign further positions Ohio’s multi-billion-dollar mobility economy to compete in this fast-growing and dynamic industry sector.”
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           About FlyOhio:
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          An initiative of DriveOhio,
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           FlyOhio
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          operates out of the Ohio Unmanned Aerial Systems (UAS) Center and seeks to make  Ohio airspace among the first in the nation ready to fly beyond line of sight. FlyOhio works with public, private, and academic partners to develop, test, and deploy the technology needed for drones to safely fly long distances without fear of collision, ultimately bringing the use of unmanned aircraft for freight, package, and personal transportation closer to reality.
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          ANRA Technologies 
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           Original post
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          by Ohio Department of Transportation.
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      <pubDate>Mon, 17 May 2021 14:36:53 GMT</pubDate>
      <guid>https://www.calanalytics.com/nasa-to-help-local-governments-plan-for-advanced-air-mobility</guid>
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      <title>Drone first responder: Hilliard police working with prototype 'game-changer'</title>
      <link>https://www.calanalytics.com/drone-first-responder-hilliard-police-working-with-prototype-game-changer</link>
      <description>CAL Analytics develops air traffic management system for drones as first responder (DFR).</description>
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         CAL Analytics develops air traffic management system for DFRs (Drones as First Responder)
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         The Hilliard Division of Police is testing a prototype drone first responder being developed by Hilliard-based Converge Technologies in a collaboration that is "a game-changer in every response,” Deputy Chief Eric Grile said. 
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          Hilliard police have been using drones since 2019, but they are deployed only after officers arrive at a scene. They also have limited applications because the Federal Aviation Administration requires drones to be visible at all times to operators or to a human in radio contact with an operator. 
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          The goal is to do incremental testing to improve the capabilities of drone first responders, earn FAA certificates of authorization after each test and to eventually achieve the ability to operate DFRs using electronic visual lines of sight, Grile said. 
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          “At this stage, it is a development partnership” and there is no pending contract for the lease or purchase of DFRs, he said. 
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          Converge Technologies, 4621 Lyman Drive, with support from other companies housed there, is developing the means for drones to safely fly to emergency scenes, avoiding birds, trees, other drones, and any other obstacles, said John Bair, the company’s chief executive officer and chief technology officer. 
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          He said Converge Technologies is building the platform for DFRs, but other companies are contributing to all the parts and pieces needed to achieve the goal of getting FAA approval for drones to fly using electronic visual lines of sight. 
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          Andrew Merz, a mechanical and material engineer at Converge Technologies, holds a prototype of a drone first responder.
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          GhostWave is developing the radar system for the DFRs; Lighthouse Avionics builds the towers necessary for the autonomy of the drones and establishing an electronic visual line of sight; Cal Analytics is building the management system or “flight control” for the DFRs; Axis Communications makes ground-based or tower-mounted cameras for tracking the DFRs; and Ubihere will establish the protocols or the “brains” of the DFRs, Bair said. 
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          “It’s a collaborative effort, (and) it’s a minimal one-year process,” he said. 
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          The companies involved in developing the drone first responders have received grants from the Ohio Federal Research Network and other agencies, Bair said.
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          "There are a lot of moving parts to this project with a lot of support from a number of private, state and federal agencies," Bair said. "To complete the entire system will take more investment over the next couple of years."
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          Besides $2.6 million in funding from the OFRN, the project also has received funding from Small Business Innovation Research, Small Business Technology Transfer, and Technology Validation Startup Fund.
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          The OFRN grant allowed GhoseWave to invest $1.2 million to develop the radar-threat detection system and Cal Analytics to invest $1,4 million to develop the air-traffic management system.
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          The SBIR funded Ubihere $850,000 to develop an asset-tracking device that will be used in the towers and on the drone, and the STTR funded Ubihere $150,000 to develop the GPS-denied navigation technology to be used in the drone.
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          The TVSF funded Lighthouse Avionics $100,000 to develop the autonomous-drone technology.
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          Converge Technologies is investing an additional $500,000 to complete the DFR prototype aircraft, Cal Analytics is investing an additional $450,000, and Lighthouse Avionics an additional $500,000, Bair said.
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          While Converge Technologies brings the computer hardware and software to the table to perfect the DFRs, Grile said the police department brings the ability to obtain the certificates of authorization needed from the FAA to operate the drones. 
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          Grile said the idea of using a drone in a more proactive way was born at the International Chiefs of Police conference that he and Chief Robert Fisher attended in Chicago in 2019. He said he learned that police in Chula Vista, California, use drones when responding to emergency situations, including car crashes, active breaking-and-entering incidents and in some instances, domestic disputes. 
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          Grile said he can envision a multitude of ways in which a drone first responder could be used. For instance, he said, when a 911 is placed, an officer could deploy a DFR that could follow a direct path at speeds of up to 80 mph and reach any location in Hilliard in about a minute.  
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          At that point, a dispatcher could choose to end the call with the person who first called 911 and begin relaying what the DFR “sees” to the officers responding to the scene. 
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          The DFR “extends the learning curve,” for responding officers, Grile said. 
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          The more an officer knows before arriving at a scene, the safer it is for the officer, those who called police, and the public, Grile said. 
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          The use of a DFR also lessens the risk of vital information being missed, misunderstood, or “lost in translation,” he said.  
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          The policies and protocols for DFRs – once finalized – must be able to be replicated so other law-enforcement agencies can use the technology, perhaps similar to how multiple police agencies began using radio frequencies, Grile said. 
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          The collaboration between the city and Converge Technologies is an example of the benefit of having such resources in the city, Development Director David Meadows said. 
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          “We’re always happy when the city can find ways to partner with companies like this because it shows that Hilliard is an attractive place for entrepreneurs to launch and grow their businesses,” Meadows said. 
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          Source:
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           This Week News
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           kcorvo@thisweeknews.con 
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           @ThisWeekCorvo 
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      <pubDate>Mon, 26 Apr 2021 14:45:20 GMT</pubDate>
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      <title>CAL Analytics launches drone safety platform to reduce risk</title>
      <link>https://www.calanalytics.com/cal-analytics-launches-drone-safety-platform-to-reduce-risk</link>
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         CAL Analytics has launched its drone safety platform and detect and avoid services to improve the safety of drone flights. The platform results from a collaborative effort between CAL Analytics, ResilienX, TrueWeather Solutions, Kongsberg Geospatial, Kent State University, and Ohio State University.
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           Late last month
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          , CAL Analytics tested its regional detect and avoid system for drones flying in unmanned traffic management (UTM) environments. Today it announced that this system has gone live to customers.
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          Ever since CAL Analytics opened up shop ten years ago, it has been working with drones and learning everything about them. Now that the commercial drone industry has really taken off, the company will continue its work in the area, falling back in its decade-long expertise.
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          A great benefit of the CAL Analytics service is the lack of onboard systems and sensors needed. This way, all drones will detect and avoid other aircraft without needing to be equipped with additional hardware.
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          Dr. Sean Calhoun, managing director of CAL Analytics said:
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          “We’re excited to work directly with the Ohio Federal Research Network, FAA, academia, and our great industry partners to bring together some of the most innovative companies in the industry to tackle operational safety in UTM. Validating a contingency management approach is a critical, but often overlooked step to achieve routine commercial drone operations.”
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          The system also integrates with UAS service suppliers (USS) to receive real-time data from the drone and send it to the detect and avoid system. This allows the system to automatically command the drone where it should go to avoid a collision. CAL Analytics has been working with AiRXOS to help with the development of the automation system.
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          The FAA’s ACAS sXu software aims to protect all aircraft in the air, no matter what platform it uses or the type of aircraft. The software solution has also been chosen as it’s much more effective than having to equip all aircraft with hardware which allows the system to scale quickly.
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          Calhoun went on to say:
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          “Additionally, our Detect and Avoid (DAA) service, powered by the FAA’s Airborne Collision Avoidance System sXu software, enables the drone/UAS to ‘see and avoid’ by using sensor and guidance technology. Having a DAA solution that does not have to be mounted on the aircraft provides much-needed relief to airborne equipage requirements. It allows for a more extensible and less tightly coupled DAA solution. Until airborne DAA sensors can be matured to the point they provide affordable and reliable surveillance services, this scalable DAA service will provide much-needed protections against airborne hazards for UAS to safely operate beyond visual line-of-sight without ground observers.”
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          Originally posted by
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           DroneDJ
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          .
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      <pubDate>Wed, 18 Nov 2020 12:23:54 GMT</pubDate>
      <guid>https://www.calanalytics.com/cal-analytics-launches-drone-safety-platform-to-reduce-risk</guid>
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      <title>Ohio Company Demonstrates Detect and Avoid Capability for Drones</title>
      <link>https://www.calanalytics.com/ohio-company-demonstrates-detect-and-avoid-capability-for-drones</link>
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         CAL Analytics Automated Collision Avoidance Technology Provides Scalable Solution for UAS Traffic Management
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         Beavercreek, OH –
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          CAL Analytics
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         recently tested a regional detect and avoid (DAA) system as a service capability for Unmanned Traffic Management (UTM) environments. The two-day demo showcased the technology’s ability to prevent collisions between unmanned aircraft and other airborne traffic in a variety of real-world scenarios.
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          CAL Analytics DAA service, powered by the FAA’s ACAS sXu software (Airborne Collision Avoidance System), enables the UAS to “see and avoid” using sensor and guidance technology that do not have to be equipped on the aircraft, thereby providing much needed relief to airborne equipage requirements. Until airborne DAA sensors can be matured to the point they provide affordable and reliable service, this scalable DAA service will provide much needed protections against airborne hazards for UAS to safely operate beyond visual line-of-sight (BVLOS) without ground observers.
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          “The successful flight tests are a key step to bringing routine commercial drone operations to a reality,” said Dr. Sean Calhoun, Managing Director of CAL Analytics. “By providing the UTM system-wide DAA as a service (without the need for onboard sensors), our cloud-based solution makes it easy for UAS service suppliers (USS) to provide automated DAA service with almost no overhead in any area with surveillance coverage.”
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          A key advancement of CAL’s DAA approach is the integration with a USS to receive real-time telemetry for the UAS and pass on DAA guidance commands for automated maneuvering.  CAL partnered with AiRXOS (part of GE Aviation) to achieve this full representative integration. By working with the USS directly as the gateway to the UAS, the DAA system can scale as needed real-time based on demand.
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          “Surveillance, strategic deconfliction, and tactical deconfliction, i.e. DAA, are critical services to support a broad spectrum of advanced UAS operations including Beyond Visual Line of Sight (BVLOS), says  Ted Lester, Chief Technologist, AiRXOS.  In integrating CAL Analytics DAA-as-a-service with AiRXOS’ UAS Air Mobility Platform, the flight demonstrations show a clear path to safely operating BVLOS without ground observers that’s achievable now.  AiRXOS is pleased to have helped perform this critical testing with CAL Analytics.”
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          Over the two-day live demonstration, CAL used telemetry, ground surveillance data from AiRXOS and SRC, Inc. to execute fully automated DAA guidance commands for both sUAS vs. sUAS and sUAS vs. manned aircraft. The integration of multiple UTM service providers provides system-wide layered deconfliction services including flight plan conflict detection and automated collision avoidance. 
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          “Our mission is to provide protection to the entire airspace, regardless of platform type or equipage restrictions,” said Josh Silbermann, ACAS sXu technical lead at the Johns Hopkins University - Applied Physics Lab supporting the FAA’s TCAS Program Office. “As we anticipate the introduction of automated and autonomous systems into the airspace, executing high densities of parallel critical missions, in order to maintain the level of safety that is expected of our national airspace, a robust DAA system needs to be at the heart of that airspace.”
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      <pubDate>Tue, 27 Oct 2020 15:14:43 GMT</pubDate>
      <author>info@calanalytics.com (Sean Calhoun)</author>
      <guid>https://www.calanalytics.com/ohio-company-demonstrates-detect-and-avoid-capability-for-drones</guid>
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      <title>Five Companies and NUAIR Begin Work on UTM Contingency Management</title>
      <link>https://www.calanalytics.com/five-companies-and-nuair-begin-work-on-utm-contingency-management</link>
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         CAL Analytics leads $1.6M effort to tackle operational safety for commercial UAS
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           A team of companies received $1.6 million from the Federal Aviation Administration to build and test their Contingency Management Platform at the New York Unmanned Aircraft Systems (UAS) Test Site at Griffiss International Airport in Rome, New York.
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          Though much progress has been made toward creating and testing unmanned traffic management (UTM) systems that will enable safe integration of drones into the national airspace, little work has been done to identify and prepare for potential system faults outside of aircraft malfunctions.
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           That’s the task these five companies have set out conquer, led by CAL Analytics, which will also provide systems integration, and working with Northeast UAS Airspace Integration Research, or NUAIR. ResilienX is contributing health and usage monitoring and fault mitigation software; TruWeather is providing micro-weather services; Assured Information Security (AIS) is offering a cybersecurity module and Kongsberg Geospatial is providing beyond visual line of sight airspace visualization and mission management.
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          “We’re excited to work directly with the FAA to bring together some of the most innovative companies in the industry to tackle operational safety in UTM,” said Dr. Sean Calhoun, Managing Director of CAL Analytics, as reported by UAS Weekly. “Validating a contingency management approach is a critical, but often overlooked, step to achieve routine commercial drone operations.”
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          ResilienX’s health and integrity monitoring system, called FRAIHMWORK, performs both active and passive monitoring, according to Andrew Carter, president and CTO. Passive monitoring relies on safety-critical components and services of a UTM system reporting who they are, what they are doing, and if they think they are working correctly. Active monitoring entails diving into various sensor feeds and APIs to analyze data in near-real-time and determine whether the devices are functioning properly.
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          “In research performed in conjunction with the NASA and the FAA, the UTM industry has looked at some failure modes and has unintentionally run across others,” Carter told Avionics International. “Almost all of these fault scenarios, however, are focused on what happens when the drone does something unintended, due to a drone malfunction. As drones improve, and become more automated to autonomous, we believe that the data the drone is using to make decisions will need to be quality assured. We look at the UTM ecosystem and have seen almost no work done to date on failure modes or fault scenarios outside of drone malfunctions.”
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          The first goal of the project, Carter said, is to monitor for and detect these off-nominal conditions originating off the aircraft that could affect UAS operations. The second goal is to provide situational awareness of these scenarios and their impacts to a user who can then facilitate contingency management, either through automated responses or manual procedures, such as calling a UAS operator or air traffic control.
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          “Without an understanding of what can go wrong, how to detect it when it does, and what to do about it, the UTM ecosystem is missing many traits that are often considered in safety critical system of systems,” Carter said.
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          FAA officials speaking during the UAS Symposium earlier this month touted progress toward the deployment of UTM and reemphasize their intention to publish a final policy on remote identification by December.
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          Officials from the agency’s Aircraft Certification Office and Flight Standards Service — responsible for awarding aircraft certification and operational permits, respectively — noted the new challenges involved in approving drone operations, as each part of the equation impacts the other.
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          “The role of operational reduction of risk has increased significantly with the introduction of UAS," said Earl Lawrence, executive director of the FAA’s Flight Standards Service.
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          “[The Aircraft Certification Office] and Flight Standards Service have been a lot more connected in looking at the mitigations between certification and operational. Because there is a gap there that we’ve been working towards,” said Rick Domingo, executive director of the Aircraft Certification Office.
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          Missing from that discussion was the third dimension of risk mitigation: a system that exists outside of a particular drone or operational plan that exists to reduce risk.
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          In other words, UTM.
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          “To date, neither regulators, operators nor the UAS Traffic Management (UTM) industry has really figured this out,” Carter told Avionics. “UTM is largely a risk mitigation concept. The FAA opened up an avenue to address this in version 2 of their UTM CONOPS through a concept of Performance Authorizations. Until operators can take advantage UTM as a quantified risk mitigation, complex UAS operations will not be done at scale.”
         &#xD;
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          Originally published by
          &#xD;
    &lt;a href="https://www.aviationtoday.com/2020/07/14/five-companies-nuair-begin-work-utm-contingency-management/" target="_blank"&gt;&#xD;
      
           Aviation Today
          &#xD;
    &lt;/a&gt;&#xD;
    
          .
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&lt;/div&gt;</content:encoded>
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      <pubDate>Tue, 14 Jul 2020 15:49:58 GMT</pubDate>
      <guid>https://www.calanalytics.com/five-companies-and-nuair-begin-work-on-utm-contingency-management</guid>
      <g-custom:tags type="string" />
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    <item>
      <title>Kent State Purchasing New Air Traffic Control Simulator</title>
      <link>https://www.calanalytics.com/college-of-aeronautics-and-engineering-purchasing-new-air-traffic-control-simulator</link>
      <description />
      <content:encoded>&lt;h3&gt;&#xD;
  
         Simulator will be Centerpiece of OFRN UAS Project led by CAL Analytics
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           The College of Aeronautics and Engineering at Kent State is procuring a new Air Traffic Control (ATC) simulator for fall 2020 from UFA Inc., a world-leading provider of cloud-based, advanced ATC simulation.
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          The controller training and research tools are used by air navigation service providers, military organizations and airports. The new simulator will significantly advance simulation capabilities for maintaining a preeminent air traffic control program at Kent State and advance capabilities in conducting meaningful research.
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          “We will be using our state-of-the-art ATC simulator to test Beyond Visual Line of Sight (BVLOS) capabilities and interaction of Unmanned Aerial Systems airspace (UAS) Traffic Management with the current ATC infrastructure,” Jenna Merriman, ATC lecturer at Kent State, said. “We are most excited to be a part of a project that could help in the architecture of UAS and the creation of unmanned traffic management procedures.”
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          The new simulator will be a centerpiece of Kent State’s research contributing to the Interoperability, Resiliency and Contingency Management for Ohio UAS Operations project funded by the Ohio Federal Research Network (OFRN). This is a multi-organization effort in defining Ohio unmanned traffic management, led by CAL Analytics in Dayton, including CAE faculty: Md Amiruzzaman, Jenna Merriman and Blake Stringer.
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          “We will be using our new ATC simulator to test our airspace and procedures for UTM,” Merriman said. “Some of the money awarded from OFRN was used to pay for the new simulation equipment.”
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          The College of Aeronautics and Engineering has selected UFA Inc. as the vendor that offered the best overall value. The proposed purchase agreement will be for an initial term of three years at $770,000 with an option for the university to renew the software licensing and updates for up to seven years at $60,000 each year. 
         &#xD;
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          The college anticipates the ability to increase participation in state or federal research initiatives, increase university relationships with state and federal UAS test centers and participate in Small Business Innovative Research with innovative companies and the FAA Centers of Excellence.
         &#xD;
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          This system will be used by more than 400 students in the aeronautics program and air traffic control major.
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          Originally posted by
          &#xD;
    &lt;a href="http://www.kentwired.com/article_e97d55a4-b638-11ea-9beb-bb27c202f06e.html" target="_blank"&gt;&#xD;
      
           Kent Wired
          &#xD;
    &lt;/a&gt;&#xD;
    
          .
         &#xD;
  &lt;/div&gt;&#xD;
&lt;/div&gt;</content:encoded>
      <enclosure url="https://irp-cdn.multiscreensite.com/0d617dc4/dms3rep/multi/UFA+Tower+Simulator.jpg" length="40273" type="image/jpeg" />
      <pubDate>Thu, 25 Jun 2020 14:05:36 GMT</pubDate>
      <guid>https://www.calanalytics.com/college-of-aeronautics-and-engineering-purchasing-new-air-traffic-control-simulator</guid>
      <g-custom:tags type="string" />
      <media:content medium="image" url="https://irp-cdn.multiscreensite.com/0d617dc4/dms3rep/multi/UFA+Tower+Simulator.jpg">
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    <item>
      <title>Drone Integration Work at  NY UAS Test Site</title>
      <link>https://www.calanalytics.com/drone-integration-work-at-ny-uas-test-site</link>
      <description />
      <content:encoded>&lt;h3&gt;&#xD;
  
         CAL Analytics to Lead Development and Testing of UTM Contingency Management Platform at Griffiss International Airport
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&lt;div data-rss-type="text"&gt;&#xD;
  
         Syracuse, NY – The Federal Aviation Administration (FAA) recently awarded a $1.6M contract 
         &#xD;
  &lt;span&gt;&#xD;
    
          that will advance unmanned traffic management (UTM) at the New York (NY) Unmanned 
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          Aircraft Systems (UAS) Test Site. CAL Analytics will lead a team of five commercial
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          companies in the development of a single, integrated contingency management platform (CMP) 
          &#xD;
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           for unmanned aircraft integration. Together with NUAIR and Oneida County, the companies will 
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           integrate and test their CMP technology to address specific safety and risk mitigation concerns 
          &#xD;
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           for operating UAS in the national airspace the NY UAS Test Site.
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          “We’re excited to work directly with the FAA to bring together some of the most innovative 
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           companies in the industry to tackle operational safety in UTM,” said Dr. Sean Calhoun, 
          &#xD;
    &lt;/span&gt;&#xD;
    &lt;span&gt;&#xD;
      
           Managing Director of CAL Analytics. “Validating a contingency management approach is a 
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           critical, but often overlooked, step to achieve routine commercial drone operations."
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          Cyber-physical systems-of-systems, like UTM, rely on a multitude of data from various sources 
          &#xD;
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           to make decisions, often with real-world safety implications. The CMP will offer important 
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           protection protocols and situational awareness, alerting operators of faults, failures, and severe 
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           weather to help ensure the safe flight of all unmanned aircraft.
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           CAL Analytics will lead system integration which combines:
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             Monitoring and mitigation software from ResilienX, of Syracuse, NY;
            &#xD;
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            Cybersecurity software provided by Assured Information Security (AIS) of Rome, NY;
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            Situational awareness display systems from Kongsberg Geospatial of Ottawa, ON;
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            Micro-weather services from TruWeather, also of Syracuse, NY
           &#xD;
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          Validation testing will be managed by NUAIR who manages operations at the Oneida County-
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           owned New York UAS Test Site at Griffiss International Airport in Rome, NY, one of just seven 
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           FAA-designated UAS test sites in the United States. The team will also leverage the state’s 50-
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           mile UAS Corridor installed with radars and advanced technologies to facilitate advanced drone 
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           operations.
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           “This partnership further solidifies Oneida County’s UAS Test Site as the global leader in 
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           industry research and advancement,” said Oneida County Executive Anthony J. Picente Jr. “The 
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           unmanned traffic management corridor we have been establishing from Rome to Syracuse is 
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           laying the groundwork for the future of UAS deployment, performance, safety and delivery 
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           capabilities.”
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           “Having a reliable UTM health and monitoring function is a key element in the safe integration 
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           and commercialization of unmanned aircraft,” said Andy Thurling, chief technology officer at 
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           NUAIR. “CAL, ResilienX, and TruWeather continue to be integral partners of NUAIR and the 
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           New York UAS Test Site, and we look forward to advancing routine, commercial UAS 
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           operations utilizing this program.”
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           Andrew Carter, President and CTO of ResilienX said, “Performance Authorizations, identified in 
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           v2 of the FAA’s UTM CONOPS will lead to scalable, routine commercial drone operations, a 
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           goal of the UAS corridor in New York. ResilienX is providing safety assurance though health 
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           monitoring and fault mitigation software to maintain a safe level of performance through 
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           encountered faults, failures or adverse conditions, enabling these complex ecosystems.”
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           TruWeather Solutions will deploy its dynamic platform that collects and presents real-time, 
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           predictive micro-weather analytics and insights. "TruWeather is excited to build and demonstrate 
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           our weather hazard notification services,” said CEO, Don Berchoff. “The CMP will monitor our 
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           scalable and agile TruFliteTM alert service to notify UTM providers and operators of emerging 
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           adverse conditions to alert specific drone types to take evasive action. The power of the service 
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           is other drones, not similarly impacted by the conditions, can continue flying.”
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           “AIS is thrilled to be a part of this FAA sponsored program in ensuring the cybersecurity of 
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           UTM, as a predicate for safe routine operations of UAS in the national air space,” said Scott 
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           Robidoux, chief operating officer at AIS. “AIS is leveraging its industry leading capabilities to 
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           provide the necessary cybersecurity performance monitoring of the UTM as part of its overall 
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           health and integrity.”
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           Kongsberg Geospatial will deploy IRIS UxS, a real-time airspace visualization system for 
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           beyond visual line-of-sight (BVLOS) mission management that allows a single operator to 
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           manage multiple aircraft. The system combines real-time data from a variety of sensors to create 
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           a real-time picture of the airspace where UAS are being operated.
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           “For the past few years, we’ve been working on improving safety for BVLOS UAS missions 
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           through the development of IRIS UxS,” explains Kongsberg vice president, Paige Cutland. “The 
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           IRIS system is now actively deployed for a variety of long-range mission applications including 
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           drone delivery, pipeline inspection, and emergency airspace operations.”
          &#xD;
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    &lt;/span&gt;&#xD;
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           The contract is part of the FAA’s efforts to perform vital drone integration safety work at the 
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           FAA’s federally-designated UAS test sites. The project was awarded after a competitive 
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           acquisition process and intended for “qualified companies who can work at FAA UAS testing
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           sites to forward essential integration technologies such as sense and avoid capabilities, 
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           geofencing, and unmanned traffic management (UTM.)”
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           ###
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      <pubDate>Mon, 13 Apr 2020 19:48:56 GMT</pubDate>
      <guid>https://www.calanalytics.com/drone-integration-work-at-ny-uas-test-site</guid>
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      <media:content medium="image" url="https://irp-cdn.multiscreensite.com/0d617dc4/dms3rep/multi/Drone-at-New-York-UAS-Test-Site.png">
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    <item>
      <title>Ohio Selects Team for Unmanned Aerial Systems Traffic Management Contract</title>
      <link>https://www.calanalytics.com/ohio-selects-team-for-unmanned-aerial-systems-traffic-management-contract</link>
      <description />
      <content:encoded>&lt;h3&gt;&#xD;
  
                  
  CAL Analytics Leads Drone Companies &amp;amp; Ohio Universities Partner to Win Milestone Project

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&lt;div data-rss-type="text"&gt;&#xD;
  &lt;p&gt;&#xD;
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w\:* {behavior:url(#default#VML);}
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  &lt;w:Zoom&gt;0&lt;/w:Zoom&gt;
  &lt;w:TrackMoves&gt;false&lt;/w:TrackMoves&gt;
  &lt;w:TrackFormatting&gt;&lt;/w:TrackFormatting&gt;
  &lt;w:PunctuationKerning&gt;&lt;/w:PunctuationKerning&gt;
  &lt;w:ValidateAgainstSchemas&gt;&lt;/w:ValidateAgainstSchemas&gt;
  &lt;w:SaveIfXMLInvalid&gt;false&lt;/w:SaveIfXMLInvalid&gt;
  &lt;w:IgnoreMixedContent&gt;false&lt;/w:IgnoreMixedContent&gt;
  &lt;w:AlwaysShowPlaceholderText&gt;false&lt;/w:AlwaysShowPlaceholderText&gt;
  &lt;w:DoNotPromoteQF&gt;&lt;/w:DoNotPromoteQF&gt;
  &lt;w:LidThemeOther&gt;EN-US&lt;/w:LidThemeOther&gt;
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  &lt;w:Compatibility&gt;
   &lt;w:BreakWrappedTables&gt;&lt;/w:BreakWrappedTables&gt;
   &lt;w:SnapToGridInCell&gt;&lt;/w:SnapToGridInCell&gt;
   &lt;w:WrapTextWithPunct&gt;&lt;/w:WrapTextWithPunct&gt;
   &lt;w:UseAsianBreakRules&gt;&lt;/w:UseAsianBreakRules&gt;
   &lt;w:DontGrowAutofit&gt;&lt;/w:DontGrowAutofit&gt;
   &lt;w:SplitPgBreakAndParaMark&gt;&lt;/w:SplitPgBreakAndParaMark&gt;
   &lt;w:EnableOpenTypeKerning&gt;&lt;/w:EnableOpenTypeKerning&gt;
   &lt;w:DontFlipMirrorIndents&gt;&lt;/w:DontFlipMirrorIndents&gt;
   &lt;w:OverrideTableStyleHps&gt;&lt;/w:OverrideTableStyleHps&gt;
   &lt;w:UseFELayout&gt;&lt;/w:UseFELayout&gt;
  &lt;/w:Compatibility&gt;
  &lt;w:DoNotOptimizeForBrowser&gt;&lt;/w:DoNotOptimizeForBrowser&gt;
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   &lt;m:dispDef&gt;&lt;/m:dispDef&gt;
   &lt;m:lMargin m:val="0"&gt;&lt;/m:lMargin&gt;
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   &lt;m:defJc m:val="centerGroup"&gt;&lt;/m:defJc&gt;
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   &lt;m:intLim m:val="subSup"&gt;&lt;/m:intLim&gt;
   &lt;m:naryLim m:val="undOvr"&gt;&lt;/m:naryLim&gt;
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  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 7"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table List 8"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table 3D effects 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table 3D effects 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table 3D effects 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Contemporary"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Elegant"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Professional"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Subtle 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Subtle 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Web 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Web 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Web 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Balloon Text"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="39" Name="Table Grid"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
   Name="Table Theme"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" SemiHidden="true" Name="Revision"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="34" QFormat="true"
   Name="List Paragraph"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="30" QFormat="true"
   Name="Intense Quote"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="19" QFormat="true"
   Name="Subtle Emphasis"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="21" QFormat="true"
   Name="Intense Emphasis"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="31" QFormat="true"
   Name="Subtle Reference"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="32" QFormat="true"
   Name="Intense Reference"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="37" SemiHidden="true"
   UnhideWhenUsed="true" Name="Bibliography"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="39" SemiHidden="true"
   UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="41" Name="Plain Table 1"&gt;&lt;/w:LsdException&gt;
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  &lt;w:LsdException Locked="false" Priority="43" Name="Plain Table 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="44" Name="Plain Table 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="45" Name="Plain Table 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="40" Name="Grid Table Light"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"&gt;&lt;/w:LsdException&gt;
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  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46"
   Name="Grid Table 1 Light Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51"
   Name="Grid Table 6 Colorful Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52"
   Name="Grid Table 7 Colorful Accent 1"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46"
   Name="Grid Table 1 Light Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51"
   Name="Grid Table 6 Colorful Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52"
   Name="Grid Table 7 Colorful Accent 2"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46"
   Name="Grid Table 1 Light Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51"
   Name="Grid Table 6 Colorful Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52"
   Name="Grid Table 7 Colorful Accent 3"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46"
   Name="Grid Table 1 Light Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51"
   Name="Grid Table 6 Colorful Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52"
   Name="Grid Table 7 Colorful Accent 4"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46"
   Name="Grid Table 1 Light Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51"
   Name="Grid Table 6 Colorful Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52"
   Name="Grid Table 7 Colorful Accent 5"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="46"
   Name="Grid Table 1 Light Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="51"
   Name="Grid Table 6 Colorful Accent 6"&gt;&lt;/w:LsdException&gt;
  &lt;w:LsdException Locked="false" Priority="52"
   Name="Grid Table 7 Colorful Accent 6"&gt;&lt;/w:LsdException&gt;
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  &lt;p&gt;&#xD;
    
                    
    Beavercreek, OH –  CAL Analytics recently secured a $1.4M
contract with the Ohio Federal Research Network (OFRN) to lead the development
of a contingency management platform (CMP) for beyond visual line of sight
drone operations. 
    
                    &#xD;
    &lt;i&gt;&#xD;
      
                      
      Interoperability, Resiliency and Contingency Management
for Ohio UAS Operations
    
                    &#xD;
    &lt;/i&gt;&#xD;
    
                    
     is 
    
                    &#xD;
    &lt;a href="https://www.ohiofrn.org/2020/02/25/the-ohio-federal-research-network-awards-7-5-million-in-grants-to-advance-unmanned-aerial-systems-innovations/"&gt;&#xD;
      &lt;span&gt;&#xD;
        
                        
        one of six projects awarded
      
                      &#xD;
      &lt;/span&gt;&#xD;
    &lt;/a&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
       in round four of OFRN’s Sustaining
Ohio Aeronautical Readiness and Innovation Next Generation (SOARING)
initiative. The collaborative effort brings together private
companies--ResilienX, TruWeather Solutions, Kongsberg Geospatial, with higher
education partners-- Kent State University, and The Ohio State University.
    
                    &#xD;
    &lt;/span&gt;&#xD;
  &lt;/p&gt;&#xD;
  &lt;p&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
      OFRN
is a program of the Wright State Applied Research Corporation, and has the
mission to stimulate Ohio’s innovation economy through job and product creation
by building statewide collaborations between university researchers, Ohio-based
federal laboratories and businesses. OFRN’s SOARING initiative leverages
funding from Ohio’s unique aerospace assets in overcoming critical technical
barriers and business challenges to enable more widespread adoption of UAS into
the national airspace.
    
                    &#xD;
    &lt;/span&gt;&#xD;
  &lt;/p&gt;&#xD;
  &lt;p&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
      As
the prime contractor, 
    
                    &#xD;
    &lt;/span&gt;&#xD;
    &lt;a href="http://www.calanalytics.com/%2523home"&gt;&#xD;
      &lt;span&gt;&#xD;
        
                        
        CAL
Analytics
      
                      &#xD;
      &lt;/span&gt;&#xD;
    &lt;/a&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
      
will lead system integration on the ground in Ohio, deploying the CMP to two customers:
the Ohio Department of Transportation in Columbus, and the Air Force Research
Lab in Springfield. 
    
                    &#xD;
    &lt;/span&gt;&#xD;
  &lt;/p&gt;&#xD;
  &lt;p&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
      “We
are really excited about this project, as it will start layering in some of the
safety measures and procedures that are critical to operationalize routine UAS
operations,” said Sean Calhoun, managing director of CAL Analytics and project
lead. “We think this project will put a nice spotlight on Ohio and all the
great UAS development work that is happening here.” 
    
                    &#xD;
    &lt;/span&gt;&#xD;
  &lt;/p&gt;&#xD;
  &lt;p&gt;&#xD;
    &lt;a href="https://www.resilienx.com/"&gt;&#xD;
      &lt;span&gt;&#xD;
        
                        
        ResilienX,
      
                      &#xD;
      &lt;/span&gt;&#xD;
    &lt;/a&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
       will contribute its proprietary
software tool, FRAIHMWORK, that provides a robust health and integrity
monitoring platform to ensure the safety and resiliency of networked systems. 
    
                    &#xD;
    &lt;/span&gt;&#xD;
  &lt;/p&gt;&#xD;
  &lt;p&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
      “
    
                    &#xD;
    &lt;/span&gt;&#xD;
    &lt;span&gt;&#xD;
      
                      
      From our experience working with
drones in the national air space, we know there are two difficult questions
that the industry will have to answer: How do you know everything is working
correctly? And, what do you do when something goes wrong? Deploying our CMP in
Ohio is the first step to answering these important questions for this
burgeoning industry,” said Andrew Carter, president &amp;amp; CTO of ResilienX.
    
                    &#xD;
    &lt;/span&gt;&#xD;
  &lt;/p&gt;&#xD;
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      Geospatial software company, 
    
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        Kongsberg
Geospatial
      
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      ,
will contribute IRIS UxS: a real-time airspace visualization system for Beyond
Visual Line-of-Sight (BVLOS) mission management that allows a single operator
to manage multiple aircraft. The system combines live data from a variety of
sensors to create a real-time picture of the airspace where UAS are being
operated. 
    
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      “For
the past few years, we’ve been working on improving safety for BVLOS UAS
missions through the development of IRIS UxS,” said Company Vice President,
Paige Cutland. “The IRIS system is now actively deployed for a variety of
long-range mission applications including drone delivery, pipeline inspection
and emergency airspace operations.”
    
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        TruWeather Solutions
      
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       will deploy its dynamic platform that
collects and presents real-time, predictive micro-weather analytics and
insights. 
    
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      "Our goal is to be the best UAS
weather service in the world,” said Don Berchoff, CEO of TruWeather. “To be the
best in a data-centric business, your data better be as accurate as possible, trusted
and reliable. Our collaboration with Ohio will raise TruWeather to the
forefront as a data-trusted micro-weather service to keep people on the ground
safe and airframes productive."
    
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      The
project also includes two Ohio-based universities. Kent State brings extensive
knowledge of air traffic control (ATC) and the national airspace. 
    
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      “We
will be using our state-of-the-art ATC Simulator to test BVLOS capabilities and
interaction of UAS Traffic Management with the current ATC infrastructure,”
said Jenna Merriman, a lecturer with the University. “We are most excited to be
a part of a project that could help in the architecture of UAS airspace.”
    
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      Ohio
State was selected for its expertise in resilience engineering and
human-autonomy teaming. 
    
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      Dr. Martijn IJtsma, assistant-professor at OSU
    
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      ’s Integrated Systems Engineering
department said, “OSU has a long and successful track record studying
resilience in complex, safety-critical systems. One of the challenges for
contingency management in UTM is to design the system and 
    
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      its
operations to support fluent coordination and adaptation between multiple
distributed actors. We use modeling and simulation techniques to identify how
we can support joint activity during edge case scenarios and make
recommendations for creating a resilient UTM system.”
    
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      The
project is a few weeks into the 18-month period of performance and is expected
to wrap up during the summer of 2021 with final demonstrations to take place in
both Springfield and Columbus, Ohio.
    
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      <pubDate>Thu, 26 Mar 2020 19:23:37 GMT</pubDate>
      <guid>https://www.calanalytics.com/ohio-selects-team-for-unmanned-aerial-systems-traffic-management-contract</guid>
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    <item>
      <title>Navigation progress for indoors and UAVs</title>
      <link>https://www.calanalytics.com/navigation-progress-for-indoors-and-uavs</link>
      <description />
      <content:encoded>&lt;h3&gt;&#xD;
  
         CAL Analytics Presents Detect and Avoid Research at IEEE/ION PLANS
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          I didn’t get to this year’s IEEE/ION PLANS meeting in Savannah, Georgia, in April, but I did find a few papers that interested me. You might have read past articles of mine that looked at the challenges of indoor navigation. And, of course, unmanned vehicles technology also is one of my favorites.
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           So, I was pleased to find papers that addressed a few key issues for me:
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             An approach that employs cooperative smartphones to achieve about 3 meters indoor location.
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             Another look at the problems in using smartphone embedded GNSS for RTK positioning.
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             Relative positioning between UAVs using GNSS, radio and inertial, and also adding image processing in a GNSS denied environment.
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             Analysis of encounter-alerting issues for UAV detect and avoid systems.
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            Indoor navigation
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           Indoor navigation is an area which is seeing quite intense research, and several companies have now put initial products on the market. The general approach has been to use sensors within smartphones combined with radio-frequency (RF) signals which seem to be readily available in stores and malls which indoor location is finding commercial applications.
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           If a position can be generated by an internal GNSS receiver within the phone in an outdoor setting prior to entering a building, the trick is to carry that position forward as GNSS signals disappear when the user moves away from the entry area. Inertial sensors in the phone are usually not accurate enough to do this job on their own, so ranging using RF from Bluetooth and Wi-Fi transmitters/beacons may be integrated to provide a position solution. Magnetic sensors in the phone have also been used to detect fixed metal structures within a building and use this data to aid location determination.
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           The problem is that you need an up-to-date database of where the Wi-Fi and Bluetooth are located, and it has been taking a lot of work to map or “fingerprint” the interiors of buildings — and guess what, these “beacons” often are moved after a mall or store is mapped, so RF ranging can become quite inaccurate.
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           So, fearless investigators from the University of Buckingham and University of Northampton in the U.K. have come up with the concept of using ranging between cooperative smartphones to aid each other and achieve location accuracies of 5-10 meters.
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           While outdoors with good GNSS position, the inertial sensors in each phone are calibrated, each phone gets position using its internal GPS and a network is formed between the phones using their relative positions. Then when a phone goes inside the building, step counting is used to maintain relative positioning in the network. This can result in around 3 meters positioning for the interior phone.
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           Well, yes, not everyone has two other buddies waiting around so one guy can go in and find the classic comic store, but for applications such as firefighters, urgent/health care, and security/police, this approach might work well.
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           Another paper looked hard at the options there might be to resolve problems with GPS performance which has previously precluded running RTK on smartphones. If we could achieve centimeter positioning on a mass-market basis, many current applications which are inhibited by cost, could become possible and revolutionize even the way we live. People have already used external solutions to solve some of the problems, but leading researchers at Texas U, with Broadcom and Radiosense support, may have come up with a self-contained solution.
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           It is known that there are issues with the capability of the GNSS chip and oscillator components in smartphones — the observables they produce are not currently of sufficient quality to sustain RTK performance. So these researchers worked with Broadcom, who supplied them with an Android smartphone, which provided access to raw code and carrier-phase outputs and was also able to process these measurements internally.
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           Carrier phase measurements in smartphones suffer from five anomalies not found in survey-grade GNSS receivers — but four of these can be fixed in post-processing. The remaining phase measurement error increases with time and precludes RTK centimeter-level positioning — it could be the result of round-off error due to processing limitations. Otherwise it seems possible that carrier-phase differential GNSS positioning might be achievable.
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           However, the researchers also studied antenna performance and found that its gain pattern was significantly affected by strong local multipath. The impact is that deep, unpredictable fading and large phase error will compromise centimeter-accurate positioning.
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           So we’re not quite there yet, but with a new smartphone version showing up almost every other year, it is always possible that researchers and manufacturers will eventually evolve designs in the right direction, and ultimately solve the problem.
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            Unmanned aerial vehicles
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           Meanwhile, researchers at West Virginia University have been investigating methods to maintain relative positioning between UAVs in flight. With drone “swarms” and cooperative drone missions becoming more common, if a simple method could be derived to maintain relative separation, these applications could become more prevalent, especially in a GPS denied environment.
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           So, with only noisy ranging radios between UAVs, and an onboard navigation system solution on each vehicle, the researchers set about developing an algorithm which can maintain relative position. The solution is complicated by the geometry between the UAVs, how often range measurements are made, and the noise in those measurements. To constrain these variables, the study was run assuming the UAVs travel at the same altitude.
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           The study concluded that— provided the UAVs travel in the same direction, parallel to each other — that their algorithm could find a solution all the time. The focus of the study appears to be on determining hearing and relative bearing between the vehicles and results were varied depending on the frequency of range measurements, the amount of noise and the geometry. So a few steps forward along the path towards making drones work together in a hostile environment where GPS is jammed. (See “Cooperative Relative Localization for Moving UAVs with Single Link Range Measurements,” J. Strader, Y.Gu, J.N. Gross, M. De Petrillo, J. Hardy.)
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           Another study on the same problem of maintaining relative position between drones was also undertaken by West Virginia University, Systems &amp;amp; Technology Research and the Air Force Research Laboratory. However, their solution didn’t only use ranging between vehicles. It took advantage of inertial measurements on each drone, computer vision calculations derived from downwards looking cameras on both UAVs, and finally magnetometer measurements were also added into a Kalman filter solution.
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           With several additional sensor measurements, the researchers were able to predict that relative positioning could be maintained in a GPS denied environment. They also considered ranging radio, magnetometer and vision update rates, and the performance/update rate of various quality inertial sensors. The principle objective is to enable accurate target hand-off between drones as one approaches the other. Overall, they found their model could support 10-meter-level position and 0.5 degree accuracy.
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           Finally, for safe operation of UAVs in the U.S. National Airspace System (NAS), minimum Detect and Avoid (DAA) standards for small to medium size UAVs are being developed for operations within drone-accessible airspace. DAA has to provide the “see and avoid” for unmanned aircraft systems (UAS) that pilots of manned aircraft use to avoid other aircraft. So surveillance sensor information needs to supply the UAV and the remote Pilot in Command (PIC) operator with the situational awareness needed to remain well clear of other aircraft.
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           Part of what DAA should provide are alerts working to universal standards for all UAS.
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           The research presented by CAL Analytics and General Atomics (with technical support and guidance by RTCA committee SC-228 and NASA) outlined the evaluation alerts generated when other aircraft are anticipated to penetrate into a well-clear volume around a UAV.
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           Alerts can be “missed,” “late” and “early” — all of which can impair DAA performance and safety and which need to characterized and mitigated. Sensors currently under consideration for use in DAA include Automatic Dependent Surveillance Broadcast (ADS-B), active surveillance transponder and airborne radar — this study looked at ADS-B and radar and the trade-off that they provide related to desirable and undesirable alerts.This analysis will likely feed into the development of UAS DAA alerting standards and requirements.
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           Radar surveillance errors were found to increase the probability of Missed, Late, Short, Early and Incorrect Alerts, all of which is bad news for radar. ADS-B surveillance errors increased the probability of Short, Early, and Incorrect Alerts. However, ADS-B did not lower performance as much as radar — better news for ADS-B. All levels of surveillance errors were seen to increase the amount of alerting jitter, with radar seeing the most significant undesirable effects.
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           Highly reliable, proven DAA systems are likely an essential part of the safety system for UAS if they are to become a regular part of operations in the NAS. General Atomics has tested a DAA system including GA’s Due Regard Radar (DRR) aboard a U.S. Customs and Border Protection (CBP) Guardian Unmanned Aircraft System (UAS), a maritime variant of the Predator B UAV. The DAA system also includes Honeywell’s Traffic Alert and Collision Avoidance System (TCAS) and Sensor Tracker, specifically designed for DAA.
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           And, also in December of  last year, a Schiebel Camcopter S-100 flew demonstration flights with an NLR-developed AirScout Detect and Avoid System. Two helicopters flew “intruder” profiles against the UAV during the demonstration. The Camcopter S-100 flew several scenarios and “unexpectedly” encountered an intruder aircraft. The system determined in real time the corrective action to maintain separation from the intruder aircraft.
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           So, progress on indoor navigation, research towards running RTK on smartphones, relative positioning between UAVs, and advances in Detect and Avoid solutions for UAVs. Something of a mixed bag, but all promise further progress around different solutions for a number of market navigation segments.
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      <pubDate>Fri, 19 Jul 2019 20:21:16 GMT</pubDate>
      <guid>https://www.calanalytics.com/navigation-progress-for-indoors-and-uavs</guid>
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      <title>Drone Stakeholders Come Together to Plot Next Steps for UAS Integration</title>
      <link>https://www.calanalytics.com/ohio-research-project-will-monitor-traffic-with-drones7a0ce842</link>
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         CAL Analytics Participates in New York Drone Symposium to Move Industry Forward
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          More than 40 unmanned aircraft systems (UAS) stakeholders from 20 global companies recently came together with the Northeast UAS Airspace Integration Research (NUAIR) Alliance to establish key objectives to move the UAS industry forward.
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           During a two-day conference in Syracuse, N.Y., they discussed what tests will help advance the integration of UAS into the national airspace system, as well as what use-case scenarios are needed to develop safety protocols and supporting technologies to fly beyond the visual line of sight (BVLOS). Additionally, they outlined how the development and enhancement of the federal UAS test site at Griffiss International Airport in Rome, N.Y., can be best used to achieve these goals.
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           As a result of this planning, multiple UAS traffic management (UTM) use-case scenarios will take place at the New York State UAS test site in the coming months. Each scenario will be broken into multiple phases, starting with simulation and moving toward live flights. Specific tests include as follows:
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           • Through the month of March, multiple UAS service suppliers (USS) will simulate flying BVLOS to a mock accident scene. This test will demonstrate how each USS technology communicates to one universal system to improve safety and efficiency.
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           • In April, the USS will move from simulations to live flights between Griffiss International Airport and Oriskany, N.Y. During these flights, the UAS will need to communicate with weather service providers to understand flight conditions and ensure safe flights. These tests will also introduce other unmanned aircraft, testing how they communicate with and avoid one another. One aircraft will be flown directly toward the other, and each will be forced to alter their course to avoid a collision.
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           • Testing of detect-and-avoid technology will continue through the month of May, including the introduction of non-participating drones (e.g., hobbyists or media in the area). This will help to ensure the safety of all involved during an event such as an accident.
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           “Each member of our alliance is looking forward to working on the key objectives we’ve outlined and contributing to NUAIR’s overall goals and vision,” says Major General Marke F. “Hoot” Gibson (ret), CEO of NUAIR, which manages the New York UAS test site.
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           Other drone industry stakeholders in attendance included AirMap, Akrobotix, ANRA, Assured Information Security, AX Enterprize, CAL Analytics, C &amp;amp; S Cos., Crown Consulting Inc., GE Global, JHU/APL, OneSky, Measure Inc., Raytheon, SAAB, SRC, Syracuse University, Thales, TruWeather Solutions, and Unifly.
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      <pubDate>Wed, 20 Feb 2019 21:10:25 GMT</pubDate>
      <guid>https://www.calanalytics.com/ohio-research-project-will-monitor-traffic-with-drones7a0ce842</guid>
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      <title>Ohio research project will monitor traffic with drones</title>
      <link>https://www.calanalytics.com/ohio-research-project-will-monitor-traffic-with-drones</link>
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         CAL Analytics to provide...
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           COLUMBUS
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          — DriveOhio, the state’s new center for coordinating smart mobility initiatives, recently announced plans to study the use of unmanned aircraft systems (UAS), sometimes called drones, to monitor traffic and roadway conditions from the air along the 33 Smart Mobility Corridor.
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          The three-year, $5.9 million study is a partnership between DriveOhio’s UAS Center and Ohio State University’s College of Engineering.
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          “At DriveOhio, we are looking for innovative ways to integrate technology into our transportation systems. This project will help us explore the intersection between autonomous and connected vehicles on land and in the air,” said Jim Barna, executive director. “The goal is to understand how we can better manage traffic, roadway incidents, and roadway conditions using advanced technology and data analysis.”
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           Vehicle research
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          This research will include both air and ground vehicles and will complement ongoing work to test autonomous and connected vehicles along the 33 Smart Mobility Corridor, a 35-mile stretch of U.S. 33 between Dublin and East Liberty.
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          Unmanned aircraft will monitor traffic and incident response along the corridor in conjunction with the state’s current fixed-location traffic camera system. The aircraft will interact with sensors and communication equipment along the corridor to feed data into the state’s Traffic Management Center.
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          The project will also use sensors and communication devices to ensure the unmanned aircraft will not collide with each other or with manned aircraft, such as small planes and helicopters, that also use the lower altitude airspace.
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          It is estimated that as many as 5,000-manned aircraft are in the sky at any given time.
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          “One of the keys to better utilizing unmanned aircraft is to ensure they will not pose a threat to other aircraft traveling in the area. This research project will make the development of that safety system a priority so that other aircraft operations such as package delivery and air taxi services can be explored down the road,” said Fred Judson, director of DriveOhio’s UAS Center.
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           Project leaders
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          The project team will be led by DriveOhio and Ohio State University’s College of Engineering in conjunction with Cal Analytics, Gannett Fleming, AiRXOS (a GE venture), Gryphon Sensors, Transportation Research Center, Woolpert, the Ohio State University Airport, and Midwest Air Traffic Control.
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          The three-year research project is set to begin July 1.
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          “We’re excited to develop this system for Ohio, which will enable safe flight of unmanned aircraft and personal air vehicles beyond the line of sight of the operator,” said OSU professor James Gregory. “This system will pave the way towards integrating unmanned aircraft into the National Airspace System.”
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          DriveOhio was created by Gov. John Kasich on Jan. 18, as a center within the Ohio Department of Transportation to brings together infrastructure partners in Ohio with those who are developing the advanced mobility technologies needed to allow the state’s transportation system to reach its full potential.
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          The mission of the center is to support flight operations for local, state and federal government and agencies.
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          Originally published by
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           Farm and Dairy on July 12, 2018
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      <pubDate>Thu, 12 Jul 2018 00:00:00 GMT</pubDate>
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      <title>$5.9M project to study drones used for traffic management</title>
      <link>https://www.calanalytics.com/5-9m-project-to-study-drones-used-for-traffic-management</link>
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         CAL Analytics Part of Research Team Developing UTM System Along US 33 Smart Mobility Corridor
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          The 33 Smart Mobility Corridor is getting even smarter under recently announced plans to study the use of unmanned aircraft systems (UAS), or drones, to monitor traffic and roadway conditions from the air along the corridor.
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           As drone numbers soar, DriveOhio’s UAS Center is investing $5.9 million for a three-year study on how to safely fit these aircraft into an already congested airspace. Led by The Ohio State University College of Engineering, the research will include both air and ground vehicles and will complement DriveOhio’s current initiatives in autonomous and connected vehicle testing.
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           Low-altitude drones will monitor traffic and incident response along the U.S. 33 Smart Mobility Corridor, a 35-mile stretch  between Dublin and East Liberty, in conjunction with the state’s current fixed-location traffic camera system. Sensors and communication equipment will feed UAS detection and tracking data to the Ohio Department of Transportation’s Traffic Management Center. The Unmanned Traffic Management (UTM) solution will enable ODOT to respond more rapidly and effectively to situations on the road.
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           The UTM system also will ensure the drones controlled by DriveOhio’s UAS Center will not collide with each other or with manned aircraft, such as small planes and helicopters that also use the lower altitude airspace. The Federal Aviation Administration (FAA) estimates 7 million UAS commercial and hobbyist purchases by 2020. These unmanned aircraft must also interface with the 5,000-manned aircraft that are in the sky at any given time.
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           “We’re excited to develop an unmanned traffic management system for Ohio, which will enable safe flight of drones and personal air vehicles beyond the line of sight of the operator,” said Ohio State Professor and Aerospace Research Center Director Jim Gregory. “Our collaborative work will pave the way for the ultimate vision of safe flight of UAS throughout Ohio and beyond.”
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           Current FAA drone regulations require that the operator maintain the unmanned aircraft within visual line of sight at an altitude of less than 400 feet and without flying over people. These logical restrictions significantly curtail the usefulness of the range of applications that industry, academia, and public entities such as ODOT can envision.
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           Based at the Transportation Research Center in East Liberty, the UTM system under development is analogous to and draws upon the heritage of the current Air Traffic Management system for the National Airspace System. Passive radars developed by Graeme Smith, research professor at Ohio State's ElectroScience Laboratory, will enable UAS detection and tracking without contributing to radio spectrum congestion.
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           “Since there is no pilot on board, we must build a comprehensive surveillance system composed of radar transceivers as well as robust signal processing on the back end, in real time, to track and filter all targets in the area of interest,” he added. “With a dynamic UTM solution in place, it will then be possible to make the safety case to the FAA for operations over people and beyond line of sight.”
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           An effective UTM system also accounts for airspace design, traffic flow management, defined flight corridors, management of UAS flights around pop-up no-fly zones, weather conditions or environmental hazards, congestion management, path planning, and collision avoidance.
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           With the UTM system in place, the corridor will be able to support future UAS and autonomous operations such as package delivery and air taxi services.
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           “This project will further establish Ohio’s lead in UAS technology and help provide a space where the Ohio Department of Transportation, DriveOhio, researchers and developers can explore the intersection between automatous and connected ground and air vehicles,” said DriveOhio’s UAS Center Director Fred Judson. “This research will also allow ODOT to better understand the changing landscapes of technologic advancements through proactive policies and investments early on in the adoption lifecycles.”
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           Research partners include Ohio State's ElectroScience Laboratory, Cal Analytics, Gannett Fleming, AiRXOS (a GE Venture), SRC, Inc., Transportation Research Center, Inc., Woolpert, The Ohio State University Airport, and Midwest Air Traffic Control.
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      <pubDate>Wed, 20 Jun 2018 20:45:29 GMT</pubDate>
      <guid>https://www.calanalytics.com/5-9m-project-to-study-drones-used-for-traffic-management</guid>
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      <title>More eyes in the sky: ODOT testing drones to monitor traffic, road conditions</title>
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         CAL Analytics to Collaborate on DriveOhio Research Project
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          Drones will monitor traffic and road conditions along the Dublin-to-Marysville stretch of Rt. 33 in a research project complementing smart mobility tests of driverless and connected vehicles.
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           The $5.9 million, three-year study also will test ways to manage the traffic of unmanned aircraft systems themselves, looking toward a future of package delivery and air taxis, the Ohio Department of Transportation said.
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           DriveOhio, which is ODOT's smart mobility coordinating center, and Ohio State University's College of Engineering are leading the project that starts July 1. DriveOhio is overseeing the separate but complementary Smart Mobility Corridor, a $15 million research initiative funded by state and local governments to test how autonomous vehicles interact with each other and regular traffic along the 35-mile stretch of Rt. 33.
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           The drones will supplement existing fixed cameras and interact with sensors and communication equipment installed along the corridor for real-time reports of traffic conditions, ODOT said. Researchers will study how drones could interact with vehicles that are either driverless or have a driver but are connected. (The corridor project plans to install on-board sensors and communication equipment on more than 1,000 cars.)
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           “At DriveOhio, we are looking for innovative ways to integrate technology into our transportation systems. This project will help us explore the intersection between autonomous and connected vehicles on land and in the air," Jim Barna, Executive Director of DriveOhio, said in a statement. "The goal is to understand how we can better manage traffic, roadway incidents, and roadway conditions using advanced technology and data analysis.”
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           Researchers also will investigate how to ensure unmanned aircraft don't collide with each other or with small planes and helicopters piloted at low altitudes.
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           “This research project will make the development of that safety system a priority so that other aircraft operations such as package delivery and air taxi services can be explored down the road,” Fred Judson, director of DriveOhio’s unmanned aircraft center, said in a statement.
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            This is one of the first projects for Airxos, a Boston subsidiary General Electric launched Thursday to design "safe, efficient, scalable integration of air and ground space for manned and unmanned vehicles."
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           Airxos said it will install sensors and communication equipment along the stretch to feed data from the drones to ODOT's Traffic Management Center.
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           DriveOhio and Ohio State have several other collaborators on the project:
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             Cal Analytics LLC, a Dayton company developing autonomous navigation systems.
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             Gannett Fleming, a Pennsylvania engineering and traffic design firm.
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             Gryphon Skylight, a system of sensors for drone traffic management from the Syracuse, New York-based research nonprofit SRC Inc.
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             Transportation Research Center Inc., a Honda Motor Co. facility in East Liberty that's a key research center for connected and driverless vehicles for several initiatives including Smart Columbus.
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             Columbus engineering firm Woolpert.
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             Ohio State University Airport and Midwest Air Traffic Control.
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            Originally published by
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        &lt;a href="https://www.bizjournals.com/columbus/news/2018/06/08/more-eyes-in-the-sky-odot-testing-drones-to.html" target="_blank"&gt;&#xD;
          
             Columbus Business First.
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      <pubDate>Fri, 08 Jun 2018 19:35:36 GMT</pubDate>
      <guid>https://www.calanalytics.com/more-eyes-in-the-sky-odot-testing-drones-to-monitor-traffic-road-conditions</guid>
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