A STPHD-Based Multi-sensor Fusion Method

In order to extract the peaks of PHD, a novel method STPHD has been proposed recently. This method can provide more accurate target state estimates than the general clustering algorithm such as k-means clustering. This paper presents a version of STPHD for multi-sensor scene and makes two contributions. First, we generalize the STPHD algorithm to a multi-sensor scenario with an existing framework of fusion. The framework includes an association step and a fusion step. This generation can get better performance in accuracy. But the association step is time-consuming. The second contribution is a novel model for computing the cost of two sets of particles with sub-weights in the association step. The numerical simulation results show that the proposed method can significantly reduce the time cost with a very slight loss in accuracy compared with the previous methods.