Paper
22 March 1996 Adaptive wavelet detection of transients using the bootstrap
Gary A. Hewer, Wei Kuo, Lawrence A. Peterson
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gary A. Hewer, Wei Kuo, and Lawrence A. Peterson "Adaptive wavelet detection of transients using the bootstrap", Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); https://doi.org/10.1117/12.235985
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Wavelets

Signal to noise ratio

Signal detection

Interference (communication)

Sensors

Wavelet transforms

Video

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