WSN-PHD: A Novel Moving Target DOA Tracking Algorithm

This letter investigates the multitarget direction of arrival (DOA) tracking problem under wireless sensor networks (WSNs), where each node is equipped with a uniform linear array (ULA). A WSN probability hypothesis density (WSN-PHD) tracking algorithm is proposed with two key innovations: 1) an exponential accumulation (EA) fusion strategy that aggregates multinode measurements to resolve occlusions in WSNs, thereby improving tracking continuity under time-varying targets, and 2) a noncircular phase-assisted likelihood function to enhance target tracking accuracy. Numerical results show that the proposed algorithm achieves average optimal subpattern assignment (OSPA) distance and location errors reduction of 78.18% and 76.82% compared to the algorithm without EA fusion.