  In Bayesian peer-to-peer decentralized data fusion for static and dynamic
systems, the underlying estimated or communicated distributions are frequently
assumed to be homogeneous between agents. This requires each agent to process
and communicate the full global joint distribution, and thus leads to high
computation and communication costs irrespective of relevancy to specific local
objectives. This work considers a family of heterogeneous decentralized fusion
problems, where we consider the set of problems in which either the
communicated or the estimated distributions describe different, but
overlapping, states of interest that are subsets of a larger full global joint
state. 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. We further
extend existing methods for approximate conservative filtering and
decentralized fusion in heterogeneous dynamic problems. Numerical examples show
more than 99.5% potential communication reduction for heterogeneous channel
filter fusion, and a multi-target tracking simulation shows that these methods
provide consistent estimates.
