On Blackman’s Data Association Problem
Jean Dezert, Florentín Smarandache, Albena Tchamova · viXra · 2016
Modern multitarget-multisensor tracking systems involve the development of reliable methods for the data association and the fusion of multiple sensor information, and more specifically the partitioning of observations into tracks. This chapter discusses and compares the application of Dempster-Shafer Theory (DST) and the Dezert-Smarandache Theory (DSmT) methods to the fusion of multiple sensor attributes for target identification purpose. We focus our attention on the paradoxical Blackman’s association problem and propose several approaches to outperform Blackman’s solution. We clarify some preconceived ideas about the use of degree of conflict between sources as potential criterion for partitioning evidences.