Interactive Multiple Model Hazard States Prediction for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA)

摘要

This research aims at improving Detect and Avoid (DAA) functions in Unmanned Aircraft Systems (UAS) using multiple model estimation to track maneuvering intruders. This research builds on previous work that used predefined aircraft encounter trajectories. An established encounter model generates the intruder trajectories while multiple models are introduced to improve intruder dynamics estimation. A new method using the Kalman prediction phase inside the Interactive Multiple Model (IMM) algorithm is presented to estimate DAA Hazard States (time to closest point of approach, horizontal miss distance, and vertical separation). The efficiency of the trajectory estimation has direct implication on the estimation of the intruder trajectory in relation to the own aircraft. The methods described in this research can aid a certification authority in determining if a DAA system is sufficient for UAS integration into the National Airspace System.