Detection and Tracking of Aircraft from Small Unmanned Aerial Systems

Abstract

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small unmanned aerial system (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with Federal Aviation Administration well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of linear Kalman filtering and extended Kalman filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS versus fixed-wing sUAS and multirotor sUAS versus general aviation (GA) plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.