A Novel Target Recognition System in Uncertain Environment

Abstract

Aiming at the challenging issue of target recognition (TR) in uncertain environment, a soft evidence inference in dynamic Bayesian networks is presented, which not only enriches Bayesian networks theoretically but also offers more flexible and robust target recognition system by exploiting the complementary of other target attributes. The architecture of the target recognition system is designed and an algorithm for TR utilizing soft evidences inferring in dynamic Bayesian network is also advanced. Experimental results illustrate that the proposed TR approach is robust by synthesizing different target characters and amending each other with respect to different time-slices. Moreover, this method can meet the real-time requirement by deriving belief even when some target attributes data are not accessible temporarily.