Measurement Dissemination-Based Distributed Bayesian Filter Using the Latest-In-and-Full-Out Exchange Protocol for Networked Unmanned Vehicles

This paper presents a measurement dissemin-ation-based distributed Bayesian filter (DBF) for a network of unmanned ground vehicles (UGVs). The DBF utilizes the latest-in-and-full-out (LIFO) exchange protocol to disseminate the sensor measurements within local neighbors. Different from existing statistics dissemination strategies that transmit posterior distributions or likelihood functions, each UGV under LIFO only exchanges latest available measurements, which significantly reduces the transmission burden between each pair of UGVs to scale linearly with the network size. 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 then derived to estimate individual probability density function (PDF) of the static target and moving target, respectively. For the former, each UGV locally fuses the newly received measurements, while for the latter, 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 target position. The effectiveness of this method is experimentally demonstrated by the target localization via multiple mobile robots with sonar sensors in an indoor environment.