Measurement-driven probability hypothesis density filter for multi-target tracking in passive radar
Xingbao Wang, Jiang Wu · 2016
A measurement-driven probability hypothesis density (MD-PHD) filter based on track initiation is proposed in this paper to handle the incapability of PHD filter when dealing with unknown newborn intensity. Measurements are distinguished into two parts by using gating technology. The existing measurements are used to update intensity while the suspicious measurements are utilized to explore new target via track initiation. The sequential Monte Carlo (SMC) implementation of the proposed filter is derived and applied to passive scenario. The simulation results demonstrate that the proposed method not only tracks existing target accurately but also initiates new target duly without any prior knowledge of newborn intensity.