Likelihood consensus: Principles and application to distributed particle filtering

We propose a distributed method for computing the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. A consensus algorithm is used for a decentralized, iterative calculation of a sufficient statistic that describes an approximation to the JLF. After convergence of the consensus algorithm, the approximate JLF—which epitomizes the measurements of all sensors—is available at each sensor. This “likelihood consensus” method requires only communications between neighboring sensors. We implement the likelihood consensus method in a distributed particle filtering scheme. Each sensor runs a local particle filter that computes a global state estimate. The updating of the particle weights of each local particle filter uses the JLF. The performance of this distributed particle filter is demonstrated on a target tracking problem.