A Markov Random Field Model of Context for High-Level Information Fusion

Abstract

This paper presents a method for inferring threat in a military campaign through matching of battlefield entities to a doctrinal template. In this work the set of random variables denoting the possible template matches for the scenario entities is a realization of a Markov Random Field. This approach does not separate low level fusion from high level fusion but optimizes both simultaneously. The result of the added high level context is a method that is robust to false positive and false negative, or missed, sensor readings. Furthermore, the high level context helps to direct the search for the best template match. Empirical results illustrate the efficacy of the method both at identifying threats in the face of false negatives, and at negating false positives, as well as illustrating the reduced computational effort resulting from the incorporation of additional high-level context.