Skip to Main Content
We consider the problem of quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the quantized variational filtering (QVF) by jointly estimating the target position and selecting the best sensors that participate in data association. In fact, the QVF has been shown to be adapted to the communication constraints of sensor networks. Its efficiency relies on the fact that the online update of the filtering distribution and its compression are executed simultaneously. Firstly, we select the best sensor that provides satisfied data of the target and balances the energy level among all sensors and minimum node density in a local cluster. Then, we estimate the target position using the QVF algorithm. The best candidate sensors are obtained by maximizing the mutual information function under energy constraints. The efficiency of the proposed method is validated by simulation results in target tracking for wireless sensor networks.