The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-256

“Signal Detection in Incompletely Characterized Impulsive Noise using Lower--Order Statistics”

by George A. Tsihrintzis and Chrysostomos L. Nikias

March 1994

We address the problem of detection of signals of known shape but unknown strength in impulsive noise using lower--order statistics. We form a generalized likelihood ratio test which is based on moment, rather than maximum likelihood, estimates of the unknown parameters of the detection problem. We show that the moment estimates we propose are both asymptotically consistent and that the proposed generalized likelihood ratio test is asymptotically equivalent to the optimum likelihood ratio test corresponding to completely known signal and noise parameters (clairvoyant test). The proposed detection schemes can be very useful not only in the detection of sonar/radar/communication signals in impulsive interference, but also in other dual--use commercial applications.

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