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Proceedings Paper

Adaptive wavelet detection of transients using the bootstrap
Author(s): Gary A. Hewer; Wei Kuo; Lawrence A. Peterson
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Paper Abstract

A Daubechies wavelet-based bootstrap detection strategy based on the research of Carmona was applied to a set of test signals. The detector was a function of the d-scales. The adaptive detection statistics were derived using Efron's bootstrap methodology, which relieved us from having to make parametric assumptions about the underlying noise and offered a method of overcoming the constraints of modeling the detector statistics. The test set of signals used to evaluate the Daubechies/bootstrap pulse detector were generated with a Hewlett-Packard Fast Agile Signal Simulator (FASS). These video pulses, with varying signal-to-noise ratios (SNRs), included unmodulated, linear chirp, and Barker phase-code modulations baseband (IF) video pulses mixed with additive white Gaussian noise. Simulated examples illustrating the bootstrap methodology are presented, along with a complete set of constant false alarm rate (CFAR) detection statistics for the test signals. The CFAR curves clearly show that the wavelet bootstrap can adaptively detect transient pulses at low SNRs.

Paper Details

Date Published: 22 March 1996
PDF: 10 pages
Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); doi: 10.1117/12.235985
Show Author Affiliations
Gary A. Hewer, Naval Air Warfare Ctr. (United States)
Wei Kuo, Naval Air Warfare Ctr. (United States)
Lawrence A. Peterson, Naval Air Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 2762:
Wavelet Applications III
Harold H. Szu, Editor(s)

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