Investigate Detect and Avoid Track Classification and Filtering
Abstract
In this project, which is funded by the FAA ASSURE program, the research team consisting of The Ohio State University, Embry-Riddle Aeronautical University, Mississippi State University, University of North Dakota and Cal Analytics will work together to: Identify the key sources of misleading surveillance information produced by airborne and ground-based detect and avoid (DAA) systems. Develop risk modeling and analysis tools to assess the system-wide effects of false or misleading information on alerting and separation, as well as impacts on pilots in command (PIC) and air traffic operators. Provide guidance and recommendations for track classification and filter performance and safety requirements to standards bodies, including Radio Technical Commission for Aeronautics (RTCA) and American Society for Testing and Materials (ASTM) DAA working groups, and inform Federal Aviation Administration (FAA) rulemaking on DAA operations. Current guidance provided by the Federal Aviation Administration has made beyond visual line of sight (BVLOS) missions an executive priority. Key to the success of these missions is the development of DAA systems capable of providing accurate pilot in the loop, or autonomous deconfliction guidance. Current standards for DAA services provided by RTCA and ASTM do not address the requirements for system performance with respect to generation of false or misleading information to the PIC or autonomous response services of the unmanned aircraft system. This research will identify key sources of uncertainty in representative DAA architectures and assess the downstream risks and effects of spurious information on downstream system performance. Additionally, recommendations will be developed for track classification accuracy requirements that provide sufficient safety margins for enabling DAA services in support of BVLOS missions.
