Abstract
This paper focuses on the method of an adaptive detector which works against Gaussian background with unknown parameters, such as covariance matrix. The authors design a tunable detector for point-like targets by mixing the Kelly's generalized likelihood ratio test and the enhanced Rao test. The analytical expression of the false alarm probability and detection probability of the proposed detector are derived. The performances of the new scheme are assessed, and analysed in comparison with its natural counterparts. The results show that it can provide enhanced rejection capabilities of mismatched signals, at the price of a limited detection loss for matched signals.
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