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With the increasing use of autonomous unmanned aerial vehicles (UAVs), it iscritical to ensure that they are continuously tracked and controlled,especially when UAVs operate beyond the communication range of ground stations(GSs). Conventional surveillance methods for UAVs, such as satellitecommunications, ground mobile networks and radars are subject to high costs andlatency. The automatic dependent surveillance-broadcast (ADS-B) emerges as apromising method to monitor UAVs, due to the advantages of real-timecapabilities, easy deployment and affordable cost. Therefore, we employ theADS-B for UAV trajectory tracking in this work. However, the inherent noise inthe transmitted data poses an obstacle for precisely tracking UAVs. Hence, wepropose the algorithm of recurrent neural network-enhanced interacting multiplemodel-Kalman filter (RNN-enhanced IMM-KF) for UAV trajectory filtering.Specifically, the algorithm utilizes the RNN to capture the maneuveringbehavior of UAVs and the noise level in the ADS-B data. Moreover, accurate UAVtracking is achieved by adaptively adjusting the process noise matrix andobservation noise matrix of IMM-KF with the assistance of the RNN. The proposedalgorithm can facilitate GSs to make timely decisions during trajectorydeviations of UAVs and improve the airspace safety. Finally, via comprehensivesimulations, the total root mean square error of the proposed algorithmdecreases by 28.56%, compared to the traditional IMM-KF.

UAV Trajectory Tracking via RNN-enhanced IMM-KF with ADS-B Data