4 September 2009 Recursive TBM method for target classification
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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.
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Gan-lin Shan, Wei Mei, Yuan-zeng Cheng, "Recursive TBM method for target classification", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 74450Y (4 September 2009); doi: 10.1117/12.829444; https://doi.org/10.1117/12.829444

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