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Proceedings Paper

Transient signal classifier using constrained total least squares extracted features, nearest-neighbor association, and probabilistic neural networks
Author(s): Theagenis J. Abatzoglou; Hal B. Arnold; Donald F. Specht
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Paper 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.

Paper Details

Date Published: 29 October 1993
PDF: 13 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162038
Show Author Affiliations
Theagenis J. Abatzoglou, Lockheed Palo Alto Research Lab. (United States)
Hal B. Arnold, Lockheed Palo Alto Research Lab. (United States)
Donald F. Specht, Lockheed Palo Alto Research Lab. (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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