With the growing popularity of small unmanned aircraft systems (UAS), there is a significant need to enable small UAS to detect and avoid collisions with both manned and unmanned aircraft. The capabilities of ADS-B make it an attractive sensor for detect and avoid (DAA), but it is susceptible to frequency congestion. This paper quantitatively analyzes the frequency limitations of 978 MHz ADS-B. It then uses these limitations to make a recommendation for well clear in ADS-B-equipped airspace that has a high density of small UAS operations.
With the increasing demand to integrate unmanned aircraft systems (UAS) into the National Airspace System (NAS), new procedures and technologies are necessary to ensure safe airspace operations and minimize the impact of UAS on current airspace users. Currently, small UAS face limitations on their utilization in civil airspace because they do not have the ability to detect and avoid other aircraft. In this article, we will present a framework that consists of an Automatic Dependent Surveillance-Broadcast (ADS-B)-based sensor, track estimator, conflict/collision detection, and resolution that mitigates collision risk. ADS-B o↵ers long range, omni-directional intruder detection with comparatively few size, weight, power, and cost demands. The proposed conflict/collision detection and planning algorithms for conflict/collision resolution are designed in the local level frame, which is unrolled, unpitched body frame where the ownship is stationary at the center of the map. The path planning method is designed to be multi-resolutional at increasing distance from the ownship to account for both self-separation and collision avoidance thresholds. We demonstrate and validate this approach using simulated ADS-B measurements.
This paper presents a time-based path planning optimizer for separation assurance for unmanned aircraft systems (UAS). Given Automatic Dependent Surveillance-Broadcast (ADS-B) as a sensor, intruder information such as position, velocity, and identification information is available at ranges on the order of 50 nautical miles. Such long-range intruder detection facilitates path planning for separation assurance, but also poses computational challenges. The time-based path optimizer presented in this paper provides a path-planning method that takes advantage of long-range ADS-B information and addresses the associated challenges. It is capable of long-range path planning and, due to the convex formulation, is computationally efficient enough to run successively for increased robustness. The ultimate result of this research is a convex, time-based path planner that is suitable for a detectand-avoid solution on small UAS in the National Airspace System.
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