Publications

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain.

Four candidate detect-and-avoid well clear definitions for unmanned aircraft systems encountering noncooperative aircraft are evaluated using safety and operational suitability metrics. These candidates were proposed in previous research based on unmitigated collision risk, maneuver initiation ranges, and other considerations. Noncooperative aircraft refer to aircraft without a functioning transponder. One million encounters representative of the assumed operational environment for the detect-and-avoid system are simulated using a benchmark alerting and guidance algorithm as well as a pilot response model. Results demonstrate sensitivity of the safety metrics to the unmanned aircraft’s speed and the detect-and-avoid system's surveillance volume. The only candidate without a horizontal time threshold, named modified tau, outperforms the other three candidates in avoiding losses of detect and avoid well clear. Furthermore, this candidate's alerting timeline lowers the required surveillance range. This can help reduce the barrier of enabling unmanned aircraft systems' operations with low size, weight, and power sensors.