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• W3038620903 endingPage "107710" @default.
• W3038620903 startingPage "107710" @default.
• W3038620903 abstract "Adaptive detection of multichannel signals in Gaussian background is studied in this paper, for the case where the existence of training data is not assumed. Four new detectors are designed for this detection problem, by using the generalized likelihood ratio test, Rao test, Wald test and a reduced-dimension (RD) approach; their probabilities of false alarm (PFAs) and detection (PDs) are analytically deduced. These PFAs indicate that the four new detectors possess the constant false alarm rate properties against the noise covariance matrix. Experimental results show that the RD-based detector achieves larger (smaller) PDs than the other three new detectors if limited (sufficient) test data are available. When mismatched signals are encountered, the RD- and Rao-based detectors perform more robust and more sensitive, respectively, than the other two new detectors." @default.
• W3038620903 created "2020-07-10" @default.
• W3038620903 creator A5001005132 @default.
• W3038620903 date "2020-11-01" @default.
• W3038620903 modified "2023-09-23" @default.
• W3038620903 title "Adaptive detection of multichannel signals without training data" @default.
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• W3038620903 doi "https://doi.org/10.1016/j.sigpro.2020.107710" @default.
• W3038620903 hasPublicationYear "2020" @default.
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