说明：  Over-the-horizon radar (OTHR) exploits skywave propagation of high-frequency signals to detect and track targets, which are different from the conventional radar. It has received wide attention because of its wide area surveillance, long detection range, strong anti-stealth ability, the capability of the long early warning time, and so on. In OTHR, a significant problem is the effect of multipath propagation, which causes multiple detections via different propagation paths for a target with missed detections and false alarms at the receiver [1–6]. Nevertheless, the conventional tracking algorithms, such as probabilistic data association (PDA) [7–9], presume that a single-measurement per target, it may consider the other measurements of the same target as clutter, and multiple tracks are produced when a single target is present. Therefore, these methods cannot effectively solve the multipath propagation problem.
(Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.)