Abstract
To localize mobile target timely and improve the accuracy of the target state estimation in wireless sensor networks, this paper presents a novel mobile target localization algorithm. By using the mean shift algorithm to construct the proposal distribution to make use of the current measurements, and weighting particles according to the virtual hamming distances in the particle filter, this proposed algorithm improves the accuracy of the target state estimation and reduces the necessary number of samples. It has lower computation and communication cost than the conventional particle filter because of more accurate state estimate of samples. Extensive simulation results confirm that this localization scheme outperforms conventional particle filter and its localization accuracy is comparable to the unscented particle filter, but its computation cost is 50% less than that of UPF.
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