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Particle filters (or PFs) are widely used for the tracking problem in dynamic systems. Despite their remarkable tracking performance and flexibility, PFs require intensive computation and communication, which are strictly constrained in wireless sensor networks (or WSNs). Thus, distributed particle filters (or DPFs) have been studied to distribute the computational workload onto multiple nodes while minimizing the communication among them. However, weight normalization and resampling in generic PFs cause significant challenges in the distributed implementation. Few existing efforts on DPF could be implemented in a completely distributed manner. In this paper, we design a completely distributed particle filter (or CDPF) for target tracking in sensor networks, and further improve it with neighborhood estimation toward minimizing the communication cost. First, we describe the particle maintenance and propagation mechanism, by which particles are maintained on different sensor nodes and propagated along the target trajectory. Then, we design the CDPF algorithm by adjusting the order of PFs' four steps and leveraging the data aggregation during particle propagation. Finally, we develop a neighborhood estimation method to replace the measurement broadcasting and the calculation of likelihood functions. With this approximate estimation, the communication cost of DPFs can be minimized. Our experimental evaluations show that although CDPF incurs about $50%$ more estimation error than semi-distributed particle filter (or SDPF), its communication cost is lower than that of SDPF by as much as $90%$.