Abstract: For the characteristic of the nonlinear relationship between the observation information of sensor nodes and the target dynamic parameters under the real application conditions, a target tracking algorithm for wireless sensor networks based on particle swarm optimization and Metropolis-Hasting sampling particle filter was proposed. Distributed architecture is adopted in this target tracking scheme. And under the dynamic network topology, particle swarm optimization and Metropolis-Hasting sampling are introduced into the resampling period to reduce sample degeneracy. In order to achieve the goal of high-precision tracking performance, the history information is shared among the particles to reduce the correlation between the history states of a single particle, so that the particles can rapidly converge to an optimal distribution. The simulations corroborate that compared with currently existing target tracking schemes based on the technology of information particle filter and parallel particle filter, the proposed scheme can reduce the total energy consumption, while ensuring the accuracy of target tracking.