It has been demonstrated that hidden Markov models (HMMs) provide an effective architecture for classification of distinct targets from multiple target-sensor orientations. In this paper, we present a methodology for designing class-based HMMs that are well suited to the identification of targets with common physical attributes. This approach provides a means to form associations between existing target classes and data from targets never observed in training. After performing a wavefront-resonance matching-pursuits feature extraction, we present an information theoretic tree-based state-parsing algorithm to define the HMM state structure for each target class. In training, class association is determined by minimizing the statistical divergence between the target under consideration and each existing class, with a new class defined when the target is poorly matched to each existing class. The class-based HMMs are trained with data from the members of its corresponding class, and tested on previously unobserved data. Results are presented for simulated acoustic scattering data.
|Original language||English (US)|
|Title of host publication||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Number of pages||4|
|State||Published - Sep 26 2001|