Adaptive Path Planning for UAV Target Tracking with Non-Gaussian Noise

Target tracking of UAV typically assumes that the system model is linear and disturbed by Gaussian noises. However, in complex environments with multiple operational constraints, the tracking performance significantly degrades when encountering non-Gaussian noises, particularly when the system measurements rely on nonlinear parameters (such as range and bearing). To address this problem, this paper proposes an extended target tracking method incorporating path planning, which utilizes mobile UAV equipped with angle of arrival (AOA) sensors to achieve GPS-denied maneuvering target tracking. Firstly, maximum correntropy criterion extended Kalman filter (MCCEKF) is utilized to improve the accuracy of state estimation by maximizing the correlated entropy between the state estimate and the observed data. Secondly, adaptive particle swarm optimization (APSO) is utilized to optimize the UAV's path synchronously based on state estimate to obtain more effective measurements under multiple constraint conditions. Finally, maximum correntropy criterion extended Kalman filteradaptive particle swarm optimization (MCEAP) is designed to estimate the system state accurately. Simulation experiments are presented to demonstrate the accuracy and robustness of the proposed method for UAV target tracking with non-Gaussian noise under multiple constraint conditions.