Decentralized parameters estimation of chemical pollution source using wireless sensor networks

Chemical pollution source parameters estimation using wireless sensor networks in an arbitrary environment has become a topic of intensive research problem. In this paper, we propose a decentralized estimation method based on the distributed Kalman filter algorithm in sensor networks to localize a chemical source and determine its emission rate. The implementation of estimation method based on a dispersion physical model and noisy measurements of concentration. As for the severe nonlinear model, it is not handled well by Kalman filter, we make use of the unscented Kalman filter (UKF) and the distributed particle filter(DPF) independently for the algorithm. Simulation results indicate that performance of the decentralized estimation methods with DPF and UKF are better than the centralized PF method (CPF) and the DPF performs much better in estimation accuracy than UKF.