Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter - Peng JIANG; Hua-hua SONG; Guang LIN (Tongxin xuebao)
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 archi-tecture 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 dis-tribution. The simulations corroborate that compared with currently existing target tracking schemes based on the tech-nology of information particle filter and parallel particle filter, the proposed scheme can reduce the total energy consump-tion, while ensuring the accuracy of target tracking.