Decentralised Data Fusion: A Graphical Model Approach

This paper proposes the use of graphical models to describe decentralised data fusion systems. The task of decentralised data fusion is considered as a specific instance of the general distributed inference problem in which there is a single common state of interest which is (partially) observed by a number of sensor platforms. Our objective is to model and solve this problem using standard graphical model techniques. Two options for modeling the problem are considered. The model based on distributed variable cliques is found superior to a graphical model with cloned variables. The model and the messages arising through inference are compared with the well-known Channel Filter algorithm. Our approach to inference is to apply a distributed version of the Junction Tree algorithm developed by Paskin and Guestrin. The algorithms were validated in a series of simulated tracking problems.