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Distributed Bayesian filters for multi-vehicle network by using Latest-In-and-Full-Out exchange protocol of measurements

This paper presents a measurement dissemination-based distributed Bayesian filtering (DBF) approach for a network of unmanned ground vehicles (UGVs). The DBF utilizes the Latest-In-and-Full-Out (LIFO) local exchange protocol of sensor measurements for data communication within the network. Different from existing statistics dissemination-based approaches that transmit posterior distributions or likelihood functions, each UGV under LIFO only exchanges with neighboring UGVs a full communication buffer consisting of latest available measurements, which significantly reduces the transmission burden between each pair of UGVs to scale linearly with the size of the network. Under the condition of fixed and undirected topology, LIFO can guarantee non-intermittent dissemination of all measurements over the network within a finite time. Two types of LIFO-based DBF algorithms are presented to estimate individual probability density function (PDF) for a static target and for a moving target, respectively. For the static target, each UGV locally fuses the newly received measurements while for the moving target, a set of measurement history is stored and sequentially fused. The consistency of LIFO-based DBF is proved that the estimated target position converges in probability to the true position. The effectiveness of this method is demonstrated by comparing with a consensus-based distributed filter and a centralized filter in the simulation of target localization.