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Proceedings Paper

An optimal nonlinear filter for detecting non-normality in a signal using the bicoherence
Author(s): Jonathan M. Nichols; Colin Olson; Joseph Michalowicz; Frank Bucholtz
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Paper Abstract

Higher-order spectral analysis is one approach to detecting deviations from normality in a received signal. In particular the auto-bispectral density function or "bispectrum" has been used in a number of detection applications. Both Type-I and Type-II errors associated with bispectral detection schemes are well understood if the processing is performed on the received signal directly or if the signal is pre-processed by a linear, time invariant filter. However, there does not currently exist an analytical expression for the bispectrum of a non-Gaussian signal pre-processed by a nonlinear filter. In this work we derive such an expression and compare the performance of bispectral-based detection schemes using both linear and nonlinear receivers. Comparisons are presented in terms of both Type-I and Type-II detection errors using Receiver Operating Characteristic curves. It is shown that using a nonlinear receiver can offer some advantages over a linear receiver. Additionally, the nonlinear receiver is optimized using genetic programming (differential evolution) to choose the filter coefficients.

Paper Details

Date Published: 11 May 2009
PDF: 8 pages
Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 73361D (11 May 2009); doi: 10.1117/12.818483
Show Author Affiliations
Jonathan M. Nichols, Naval Research Lab. (United States)
Colin Olson, Naval Research Lab. (United States)
Joseph Michalowicz, Global Strategies Group (United States)
Frank Bucholtz, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 7336:
Signal Processing, Sensor Fusion, and Target Recognition XVIII
Ivan Kadar, Editor(s)

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