Algorithms for Optimum Detection of Signals in Gaussian Noise
NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF STATISTICS
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Algorithms are presented for detection of signals in Gaussian noise. The signals can be Gaussian or nonGaussian. The algorithms are derived from a general solution to the continuous-time problem, and are approximations to the continuous-time likelihood ratio. They do not require knowledge of the probability distributions for the signal-plus-noise process, but instead require knowledge or estimation of a function. Independent sampling is not assumed. One algorithm is fully adaptive to the signal-plus-noise process. The algorithms have the potential of providing significant performance improvements, as compared to classical detection methods, when the signal-plus-noise process is broadband stationary or nonstationary, and particularly when it is nonGaussian.
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