Transient signal classifier using constrained total least squares extracted features, nearest-neighbor association, and probabilistic neural networks

Theagenis J. Abatzoglou; Hal B. Arnold; Donald F. Specht

doi:10.1117/12.162038

29 October 1993 Transient signal classifier using constrained total least squares extracted features, nearest-neighbor association, and probabilistic neural networks

Theagenis J. Abatzoglou, Hal B. Arnold, Donald F. Specht

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Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162038
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

A signal classifier is presented whose features consist of characteristic frequencies/resonances of time series of the signa over the observation window. Signals of interest here include those which can be well approximated by a relatively small number of sinusoids; i.e. harmonic signals, impulse responses from various types of objects, etc. The relevant features of these signals are their principal frequencies, corresponding decay coefficients and amplitudes (triplets). These are extracted from the time series by an application of the CTLS method. A nearest neighbor association is implemented using a maximum entropy distance measure between the extracted feature triplet vectors and a set of characteristic triplets for each signal type. This creates an assignment between the extracted triplets to each signal characteristics triplet.

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Theagenis J. Abatzoglou, Hal B. Arnold, and Donald F. Specht "Transient signal classifier using constrained total least squares extracted features, nearest-neighbor association, and probabilistic neural networks", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162038

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