Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion
This addresses efficiency issues in decentralized systems for autonomous agents, though it is incremental as it builds on existing homogeneous methods.
The paper tackles the problem of high computation and communication costs in Bayesian decentralized data fusion by formulating heterogeneous fusion where agents handle overlapping subsets of variables, resulting in over 99.5% potential communication reduction and consistent, scalable estimates in simulations.
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.