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.

翻译：在贝叶斯点对点去中心化数据融合中，自主智能体本地持有的基础分布通常被假定为基于同一组变量（同构）。这要求每个智能体处理并通信完整的全局联合分布，因此无论与特定局部目标的相关性如何，都会导致高昂的计算和通信成本。本文提出并研究了异构去中心化融合问题，该问题定义为：通信或处理的分布描述的是不同但有重叠的随机状态子集（这些子集属于更大的全局联合状态）。我们利用此类问题的条件独立结构，严格推导了新颖的精确与近似条件因子化异构融合规则。进一步地，我们开发了同构信道滤波器算法的新版本，以实现针对动态问题中平滑与滤波场景的保守异构融合。数值示例表明，异构信道滤波器融合可降低超过99.5%的潜在通信量，多目标跟踪仿真显示，这些方法在保持计算可扩展性的同时提供了一致性估计。