Algorithms for Optimum Detection of Signals in Gaussian Noise
C. Richard Baker · Defense Technical Information Center (DTIC) · 1991
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.