A Tracking Algorithm for Sparse and Dynamic Underwater Sensor Networks

An underwater sensor network (UWSN) has sparse and dynamic characteristics. In UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) problems of a slow convergence rate low filtering accuracy. To solve these problems, tracking algorithm for UWSNs (TASD) is proposed. Firstly, estimation results local are processed by weighted average consensus (WACF). this way, reliability difference state between nodes in UWSN reasonably eliminated. Secondly, delayed update mechanism (DUM) added to WACF, which effectively solves problem time synchronization two filters. Thirdly, under condition limited communication energy consumption, an alternating random scheme (ARS) designed, optimizes mean square fusion filter. Simulation show that proposed can be applied maneuvering target effectively. Compared with method, it higher accuracy faster speed. The error TASD 91.3% lower than CF/DPF, reduced 85.6% compared CF/DPF.