Local-diffusion-based distributed SMC-PHD filtering using sensors with limited sensing range

We investigate the problem of distributed multitarget tracking by using a set of netted, collaborative sensors with limited sensing range (LSR), where each sensor runs a sequential Monte Carlo probability hypothesis density filter and exchanges relevant posterior information with its neighbors. The key challenge stems from the LSR of neighbor sensors whose fields of view (FoVs) are partially/non-overlapped, and therefore, they may observe different targets at the same time. With regard to the local common FoVs among neighbor sensors, the proposed distributed fusion approach, called local diffusion, performs one iteration of neighbor communication per filtering step in either of two means. One is given by immediate particle exchange, in which a reject-control operation is devised to reduce the number of communicating particles. The other is done by converting the particle distribution to Gaussian functions for parametric information exchange and fusion. The performance of both approaches has been experimentally investigated via simulation for different LSR situations and compared with cutting-edge approaches.