An extension of statistical decision theory with information theoretic cost functions to decision fusion: Part II

摘要

This paper is the second of two papers that examine statistical decision theory for the case where the cost function is an information theoretic cost function. Part I reviewed the information theoretic decision rule that results from the information theoretic function as well as some of its unique characteristics. It was shown that information theoretic decisions are represented by characteristic probability distributions and that the optimal decision is the one with the smallest Kullbach-Leibler statistical distance from the measurement-based probability distribution. The information theoretic decision function is less sensitive to the assignment of prior probabilities and the abstraction levels of the decision and hypothesis sets when compared to Bayesian decision theory. This second part develops an entirely new decision fusion rule from the decision rule of Part I. The decision fusion rule is restructured to produce a recursive algorithm that has similarities with the Kalman filter. The performance characteristics of the recursive algorithm and a handful of extensions are compared to a recursive Bayesian decision fusion algorithm through the analysis of a simple decision problem.