A general framework that theoretically links the higher-order correlation (HOC) domain with statistical decision theory is explored. It is then applied to the problem of classification of M-ary frequency shift keying (MFSK) signals when contaminated by additive white Gaussian noise (AWGN). In particular, we propose a novel class of classifiers that utilizes time-domain HOC operations while completely avoiding the explicit determination of the spectrum of the observed signal. It is shown that this method delivers a performance that tightly lower-bounds that of the optimal likelihood-ratio test. In addition, this intrinsically wideband HOC-based method possesses an immunity to imperfect knowledge of exact frequency locations. Substantial performance improvement is also reported over the energy-based rule whenever it is applicable.
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