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

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.