Multi-target state extraction for the particle probability hypothesis density filter

The probability hypothesis density (PHD) filter has emerged as a promising tool for dealing with the multi-target tracking problem in recent years. However, except in some special situations, closed-form recursive update equations for the PHD filter do not exist and the particle filter approaches have to be used. The output of the particle filter at each step is the particle clouds approximation of the PHD. Thus, some special algorithms are needed to extract the target states from those particles. Utilising the information of both particles´ weight and their spatial distribution, an improved algorithm named C-Clean is proposed in this study. This algorithm is comprised of two steps. First, clustering techniques are used to exploit the spatial distribution of particles. Then, within those clusters whose corresponding PHD weight is beyond some predefined threshold, the peak extraction procedure modified from the CLEAN technique is taken to extract the multi-target state. Simulation results demonstrate that its performance is better than those algorithms using the information of particles´ spatial distribution or weight only.