A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion

摘要

This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory.