Performance evaluation of fusion rules for multitarget tracking in clutter based on generalized data association

In this paper, we present and compare different fusion rules which can be used for generalized data association (GDA) for multitarget tracking (MTT) in clutter. Most of tracking methods including target identification (ID) or attribute information are based on classical tracking algorithms as PDAF, JPDAF, MHT, IMM, etc and either on the Bayesian estimation and prediction of target ID, or on fusion of target class belief assignments through the Dempster-Shafer theory (DST) and Dempster's rule of combination. In this paper we pursue our previous works on the development of a new GDA-MTT based on Dezert-Smarandache Theory (DSmT) but compare also it with standard fusion rules (Dempster's, Dubois & Prade's, Yager's) and with a new fusion Proportional Conflict Redistribution (PCR) rule in order to assess the efficiency of all these different fusion rules for this GDA-MTT in highly conflicting situation. This evaluation is based on a Monte Carlo simulation for a difficult maneuvering MTT problem in clutter similar to the example recently proposed by Bar-Shalom, Kirubarajan and Gokberk.