Target tracking based on a distributed particle filter in underwater sensor networks

In this paper, based on a distributed particle filter, two tracking algorithms are proposed for tracking mobile targets in cluster‐based underwater sensor networks (USNs). Both tracking algorithms run local particle filter sequentially at each cluster along target trajectories, but they adopt different methods of selecting measurements from sensor nodes to balance the information contribution against the cost. Performance metrics are proposed and discussed in terms of tracking performance, communication cost, energy cost, and tracking response time. Simulations are conducted to quantitatively compare the proposed algorithms as well as another tracking algorithm based on extended Kalman filter (EKF). Our results indicate that one tracking algorithm achieves higher tracking accuracy while the other achieves dramatic reduction of communication cost, energy cost, and tracking response time. Furthermore, performance of two tracking algorithms has been studied in terms of detection threshold and sensor density.