  With the increasing use of autonomous unmanned aerial vehicles (UAVs), it is
critical 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 satellite
communications, ground mobile networks and radars are subject to high costs and
latency. The automatic dependent surveillance-broadcast (ADS-B) emerges as a
promising method to monitor UAVs, due to the advantages of real-time
capabilities, easy deployment and affordable cost. Therefore, we employ the
ADS-B for UAV trajectory tracking in this work. However, the inherent noise in
the transmitted data poses an obstacle for precisely tracking UAVs. Hence, we
propose the algorithm of recurrent neural network-enhanced interacting multiple
model-Kalman filter (RNN-enhanced IMM-KF) for UAV trajectory filtering.
Specifically, the algorithm utilizes the RNN to capture the maneuvering
behavior of UAVs and the noise level in the ADS-B data. Moreover, accurate UAV
tracking is achieved by adaptively adjusting the process noise matrix and
observation noise matrix of IMM-KF with the assistance of the RNN. The proposed
algorithm can facilitate GSs to make timely decisions during trajectory
deviations of UAVs and improve the airspace safety. Finally, via comprehensive
simulations, the total root mean square error of the proposed algorithm
decreases by 28.56%, compared to the traditional IMM-KF.
