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Class-based identification of underwater targets using hidden Markov models

Publication ,  Journal Article
Dasgupta, N; Runkle, P; Carin, L
Published in: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
January 1, 2001

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.

Duke Scholars

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISSN

1520-6149

Publication Date

January 1, 2001

Volume

5

Start / End Page

3161 / 3164
 

Citation

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Dasgupta, N., Runkle, P., & Carin, L. (2001). Class-based identification of underwater targets using hidden Markov models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5, 3161–3164.
Dasgupta, N., P. Runkle, and L. Carin. “Class-based identification of underwater targets using hidden Markov models.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 5 (January 1, 2001): 3161–64.
Dasgupta N, Runkle P, Carin L. Class-based identification of underwater targets using hidden Markov models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2001 Jan 1;5:3161–4.
Dasgupta, N., et al. “Class-based identification of underwater targets using hidden Markov models.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 5, Jan. 2001, pp. 3161–64.
Dasgupta N, Runkle P, Carin L. Class-based identification of underwater targets using hidden Markov models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2001 Jan 1;5:3161–3164.

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISSN

1520-6149

Publication Date

January 1, 2001

Volume

5

Start / End Page

3161 / 3164