Recursive TBM method for target classification

Abstract

Target classification based on the transferable belief model (TBM) is believed to be more robust than the Bayesian method. However, existing TBM classifier may forget over time the estimated prior information of the class. This paper proposes a recursive TBM classifier, which could combine the current basic belief assignment (BBA) of the class with the historic class information. Besides, feature mapping from the feature space to the class space, instead of the conventional converse mapping, is utilized to improve the performance of the recursive classifier. Simulation results reveal that the proposed TBM classifier eliminated the deficiency of existing TBM method and has more robust performance than the Bayesian classifier.