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In conjunction with physics-based feature extraction, Hidden Markov Model. (HMM) classifiers have been used successfully to fuse scattering data from multiple target orientations where the target-sensor orientation is generally unknown or “hidden” . The use of prior knowledge concerning sensor motion is employed in modeling the sequential data, improving classification performance. However, the assumptions of first order Markovian state transitions state-dependent statistics constrain the intrinsic class of pdf structures admitted by the HMM, for use in classification. In-this paper we overcome the above limitation by using the local variations in the HMMs induced by each sequence of observations as the feature vector for a support vector machine. (SYM) classifier. Improved discrimination results are presented for measured acoustic scattering data.
Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on (Volume:3 )
Date of Conference: 13-17 May 2002