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

TL;DR

This paper introduces a novel RNN-enhanced IMM-KF algorithm for UAV trajectory tracking using ADS-B data, improving accuracy by adaptively filtering noisy signals and capturing maneuvering behaviors.

Contribution

It presents a new RNN-integrated IMM-KF method that adaptively adjusts noise parameters for more precise UAV tracking with ADS-B data.

Findings

Achieves 28.56% reduction in RMS error compared to traditional IMM-KF.

Effectively captures UAV maneuvering behavior and noise levels.

Enhances real-time UAV tracking accuracy and airspace safety.

Abstract

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.