Focusing on the maneuvering target tracking problem in wireless sensor networks

a state space model for describing the maneuvering target acceleration is proposed.On the basis of the model

single and multiple target tracking algorithms based on particle filtering are developed

in which the approximate posterior probability distribution is acquired through searching a set of transmitted random samples in the state space

and the integral operation is replaced by sample's average value so as to obtain the minimum variance estimation. Simulation results show that in wireless sensor network environment

the maneuvering target tracking problem can be solved better by the proposed algorithm. The precision of velocity tracking and maneuvering tracking acceleration is increased by about 27% and 20% respectively compared to the traditional particle filtering algorithms.