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Proceedings Paper

Space-based RF signal classification using adaptive wavelet features
Author(s): Michael P. Caffrey; Scott D. Briles
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Paper Abstract

Rf signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly-chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provides a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped- radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neural network classifier. The performance of the adaptive wavelet classifier is then compared to an FFT based neural network classifier.

Paper Details

Date Published: 5 July 1995
PDF: 10 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213037
Show Author Affiliations
Michael P. Caffrey, Los Alamos National Lab. (United States)
Scott D. Briles, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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