Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

TL;DR

This paper introduces exact and approximate methods for heterogeneous Bayesian decentralized data fusion, reducing communication costs and maintaining accuracy in multi-agent systems with overlapping but different local states.

Contribution

It formulates heterogeneous fusion problems, derives novel factorized fusion rules, and extends the Channel Filter algorithm for scalable, conservative data fusion in dynamic scenarios.

Findings

Over 99.5% communication reduction in channel filter fusion.

Consistent estimates in multi-target tracking with scalable computation.

Effective handling of overlapping local states in decentralized fusion.

Abstract

In Bayesian peer-to-peer decentralized data fusion, the underlying distributions held locally by autonomous agents are frequently assumed to be over the same set of variables (homogeneous). 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 formulates and studies heterogeneous decentralized fusion problems, defined as the set of problems in which either the communicated or the processed distributions describe different, but overlapping, random 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 of novel exact and approximate conditionally factorized heterogeneous fusion rules. We further develop a new version of the homogeneous Channel Filter algorithm to enable conservative heterogeneous fusion for smoothing and filtering scenarios in 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 while remaining computationally scalable.