Heterogeneous Decentralized Fusion Using Conditionally Factorized Channel Filters

This paper studies a family of heterogeneous Bayesian decentralized data fusion problems. Heterogeneous fusion considers the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest which are subsets of a larger full global joint state. On the other hand, in homogeneous decentralized fusion, each agent is required to process and communicate the full global joint distribution. This might lead to high computation and communication costs irrespective of relevancy to an agent's particular mission, for example, in autonomous multi-platform multi-target tracking problems, since the number of states scales with the number of targets and agent platforms, not with each agent’s specific local mission. In this paper, we exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate, heterogeneous, conditionally factorized channel filter methods. Numerical examples show more than 95% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.