Support Vector Machines for improved multiaspect target recognition using the fisher kernel scores of Hidden Markov Models

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” [1]. 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.