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For the multi-target tracking in clutter is difficult to remove and the problem of high complexity by the data association, the probability hypothesis density (PHD) filtering algorithm is used in a wireless sensor networks (WSN) in order to get the appropriate target state information fusion estimation. First of all, the target state model and sensor observation model are described by random finite sets theory in a wireless sensor networks (WSN) with cluster-tree topology; then, the PHD filter processes the set of real-time data, when the measured value is less than the threshold, which is set up according to the distance between target and node, so as to realize the detection and tracking of targets. The simulation results show that PHD filtering algorithm used in the wireless sensor networks has higher tracking accuracy and efficiency than the particle filtering algorithm.