Distributed Sequential Estimation in Asynchronous Wireless Sensor Networks

We propose a distributed sequential estimation scheme for wireless sensor networks with asynchronous measurements. Our scheme combines the prediction and update steps of a Bayesian filter (for time alignment and recursive state estimation) with a fusion rule (for intersensor fusion using local communication). We also propose a reduced-complexity implementation using particle filtering and Gaussian mixture approximations, and an estimator of the delays resulting from processing and communication. Simulations for a target tracking problem demonstrate the good performance of our scheme.