Non-Gaussian signal detection from multiple sensors using the bootstrap

Existing tests based on the cross bispectrum to detect stationary non-Gaussian signals use two sensors or channels of data. We propose to extend such tests to the case of multiple sensors. Our approach uses Bonferroni tests of multiple hypotheses. A multi-sensor bootstrap method is presented and com...

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Bibliographic Details
Main Authors: Hwa-Tung Ong, Zoubir, A.M.
Format: Conference Proceeding
Language:English
Subjects:
Australia
Detectors
Noise level
Random sequences
Signal detection
Signal processing
Signal to noise ratio
Statistical analysis
Statistical distributions
Testing
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Summary:Existing tests based on the cross bispectrum to detect stationary non-Gaussian signals use two sensors or channels of data. We propose to extend such tests to the case of multiple sensors. Our approach uses Bonferroni tests of multiple hypotheses. A multi-sensor bootstrap method is presented and compared through simulations with two other multi-sensor methods. Simulation results show that the bootstrap method is better able to keep the level of significance and have high correct detection (as the SNR increases) than the others.
DOI:10.1109/ICICS.1997.647116