Wavelet-domain hidden Markov models for signal detection and classification

Matthew S. Crouse; Robert D. Nowak; K. Mhirsi; Richard G. Baraniuk

doi:10.1117/12.279500

24 October 1997 Wavelet-domain hidden Markov models for signal detection and classification

Matthew S. Crouse, Robert D. Nowak, K. Mhirsi, Richard G. Baraniuk

Proceedings Volume 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII; (1997) https://doi.org/10.1117/12.279500
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

This paper addresses the problem of detection and classification of complicated signals in noise. Classical detection methods such as energy detectors and linear discriminant analysis do not perform well in many situations of practical interest. We introduce a new approach based on hidden Markov modeling in the wavelet domain. Using training data, we fit a hidden Markov model (HMM) to the wavelet transform to concisely represent its probabilistic time- frequency structure. The HMM provides a natural framework for performing likelihood ratio tests used in signal detection and classification. We compare our approach with classical methods for classification of nonlinear processes, change-point detection, and detection with unknown delay.

Citation Download Citation

Matthew S. Crouse, Robert D. Nowak, K. Mhirsi, and Richard G. Baraniuk "Wavelet-domain hidden Markov models for signal detection and classification", Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); https://doi.org/10.1117/12.279500

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