Sequential likelihood consensus and its application to distributed particle filtering with reduced communications and latency

We propose a sequential likelihood consensus (SLC) for a distributed, sequential computation of the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. The SLC is based on a novel dynamic consensus algorithm, of which only a single iteration is performed per time step. We demonstrate the application of the SLC in a distributed particle filter with low communication requirements and low latency. Because the JLF is available at each sensor, the local particle filters at the individual sensors take into account the measurements of all sensors. The performance of the proposed distributed particle filter is assessed for a target tracking problem.