3D UAV Localization and Tracking Based on Attitude Information and Motion Status Fusion

In this paper, we study a short-range unmanned aerial vehicle (UAV) localization and tracking system, where a base station (BS) equipped with a uniform rectangular array is arranged to track the UAV by continuously estimating its position and attitude. To accomplish this task, we establish a subarray-wise far-field received signal model by dividing the BS array into multiple subarrays. Then, based on a relationship between UAV’s attitude and motion status, we formulate a probabilistic UAV tracking problem that jointly estimates the UAV position and attitude in an online fashion. A new message-passing-based algorithm is proposed to solve this problem, which combines the attitude and motion status information to enhance tracking performance. Numerical results show that the proposed algorithm outperforms other alternatives, and demonstrate that the information fusion of the UAV attitude and motion status can improve the accuracy of the UAV localization and tracking.