Collaborative PHD filter for fast multi-target tracking

Considering the difference of dynamic evolution between the survival target and the newborn targets collaborative probability hypothesis density (CoPHD) filter framework for fast multi-target tracking is proposed. The framework strives to improve the systematic implementing efficiency as well as guarantee the tracking accuracy by dynamically partitioning the measurement set into two parts, survival and newborn target measurement sets in which PHD groups are updated respectively, and constituting an interactive and collaborative mechanism for the processing modules. In addition, the framework has the ability of state self-extracting by utilizing PHD group processing, and the implementation via the sequential Monte Carlo (SMC) method is presented. Simulation results show that the proposed SMC-CoPHD filter has greatly-reduced computation cost and significantly-improved state-extraction accuracy.