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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| creator | Hwa-Tung Ong Zoubir, A.M. |
| description | 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_str_mv | 10.1109/ICICS.1997.647116 |
| format | Conference Proceeding |
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| identifier | ISBN: 0780336763 |
| ispartof | Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat, 1997, Vol.1, p.340-344 vol.1 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Australia Detectors Noise level Random sequences Signal detection Signal processing Signal to noise ratio Statistical analysis Statistical distributions Testing |
| title | Non-Gaussian signal detection from multiple sensors using the bootstrap |
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