Decentralized variational filtering for simultaneous sensor localization and target tracking in binary sensor networks

Resource limitations in wireless sensor networks have put stringent constraints on distributed signal processing. In this paper, we propose a cluster-based decentralized variational filtering algorithm with minimum resource allocation for simultaneous sensor localization and target tracking. At each sampling instant, only one cluster of sensors is activated according to the prediction of the target state. Slave sensors employ a binary proximity observation model to reduce energy consumption and minimize communication cost. Based on the binary measurements between sensors and the target, activated sensors and target location estimates are interdependently improved. By adopting the variational method, the inter-cluster information exchange is reduced to one single Gaussian statistic, further minimizing resource consumption in the network. Since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved compared to the classical particle filtering. Effectiveness of the proposed approach is evaluated in terms of tracking accuracy and localization precision.