Simultaneous target tracking and sensor location refinement in distributed sensor networks

The conventional consensus filter is an effective tool for distributed fusion but so far the literature has paid little attention to take the uncertainty of sensor position into consideration. In this paper, we address this problem of sensor position uncertainty and propose variational Bayesian and consensus based filters for simultaneous target tracking and sensor location refinement in distributed sensor networks. The variational Bayesian method is employed to jointly estimate the target state and local sensor position while the consistent global target state can be approached by consensus scheme at each node. The filter for linear measurement model is first derived and then extended to nonlinear measurement models exploiting the extended Kalman filter paradigm. Simulations are performed in order to demonstrate the effectiveness of the proposed algorithms.