Out-of-Sequence Measurements Processing Based on Unscented Particle Filter for Passive Target Tracking in Sensor Networks

To improve the passive tracking performance in wireless sensor networks, we propose a processing scheme for out-of-sequence measurements (OOSMs). Dynamic clustering structure of sensor nodes is constructed according to the present position of the target. The particle-filtering scheme for OOSMs is presented in the dynamic clustering structure with the turn rate model. As an improvement of the standard particle filter, the unscented particle filter (UPF) is used to incorporate the most current measurements and to generate the proposal distribution of the particle filter, and detailed implementation steps of OOSM processing scheme based on UPF (OOSM-UPF) is deduced. The simulated scenario is constructed to compare several schemes for OOSMs. Simulation results show that the tracking performance of OOSM-UPF is much improved than other schemes