Gaussian Mixture Based Progressive Chernoff Fusion
Simone Semeraro, Keith A. LeGrand · 2024
Probabilistic decentralized data fusion is the process of combining probabilistic beliefs from multiple sensors to reduce uncertainty and is broadly applicable to problems in aerospace, robotics, and wireless sensor networks. Fusing statistical information into a fused density is especially useful in distributed sensor networks, where each node or agent possesses only limited computation and sensing capabilities. The Chernoff fusion rule prevents information double-counting and produces a fused density that is equidistant from the input densities in an information-theoretic sense. This paper presents a novel approach to Gaussian mixture Chernoff fusion based on the progressive Bayes framework, where the optimal fused mixture is obtained through homotopy continuation. An advantage of the new approach is that it does not require fitting of Gaussian mixtures to lossy samples. A new and simple strategy for determining the optimal Chernoff weighting parameter is also presented and shown to outperform more complicated methods.