In order to safely and seamlessly integrate UAS into non-segregated airspace, a robust DAA capability needs to be established. The New York Griffiss International Airport Unmanned Aircraft Systems (UAS) Test Site is evaluating next-generation UAS ground-based detect and avoid (GBDAA) systems capable of enabling extended UAS beyond line-of-sight (BLOS) operations in airport terminal areas and in transition airspace. GBDAA is an important research area in the current Phase Two of RTCA Special Committee 228 (Minimum UAS Performance Standards), which focuses on civil UAS equipped to operate under IFR flight rules in extended UAS operations in Class D, E, and G, airspace, down to but not including ground operations. The Griffiss UAS Test Site has set up an instrumented test range for air traffic surveillance, extending from the Griffiss airport and its Class D airspace to about 40 NM to the north. The Griffiss range instrumentation system employs multi-sensor fusion using a combination of wide area multilateration, ADS-B, and primary radar to track cooperative and noncooperative air traffic. The system is designed to operate in combination with other air traffic surveillance sensors, including airborne DAA sensors. Griffiss test range instrumentation provides real-time air traffic surveillance, with an ability to incorporate simulated air traffic. The system incorporates a data collection, storage, and analysis capability for building the safety for UAS integration into terminal and transition airspace, and supports live, virtual and constructive simulation in distributed environments (LVC-DE). To understand the system and its uses, it is important to keep sight of reasons for capabilities such as those under development at the Griffiss UAS Test Site. The paper concludes with a description of an example of how multi-sensor fusion in the Griffiss range instrumentation system might be employed to make a safety case of beyond line-of-sight UAS operation in Griffiss test range airspace.
Multi-sensor fusion for UAS ground-based detect and avoid capability
2017-04-01
1992099 byte
Conference paper
Electronic Resource
English
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