Title :
A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion
Author :
Buede, Dennis M. ; Girardi, Paul
Author_Institution :
Dept. of Syst. Eng., George Mason Univ., Fairfax, VA, USA
fDate :
9/1/1997 12:00:00 AM
Abstract :
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
Keywords :
Bayes methods; aircraft; case-based reasoning; convergence; object recognition; probability; radar target recognition; sensor fusion; uncertainty handling; Bayesian reasoning; Dempster-Shafer theory; aircraft recognition; convergence time; evidence theory; evidential reasoning; multisensor fusion; probability; target identification; uncertainty handling; Aircraft; Bandwidth; Bayesian methods; Multisensor systems; Power measurement; Radar tracking; Sensor fusion; Sensor systems; Target tracking; Uncertainty;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/3468.618256