Higher-order correlation-based classification of asynchronous MFSK signals

The problem of asynchronous classification of M-ary frequency shift keying (MFSK) signals when contaminated by additive white Gaussian noise (AWGN) is addressed. Two approaches are adopted. The first is based on the classical likelihood-ratio theory which provides performance that is optimal but sensitive to unknown frequency offsets. The second completely eliminates the fixed-frequency structure and instead utilizes measurements made strictly in the higher-order correlation (HOC) domain. Assessed are the performance sensitivity gaps of the synchronous rules to unknown signal time of arrival or epoch offsets. This sensitivity is ameliorated by averaging over a reduced-uncertainty epoch model. Fairly satisfactory results are reported with small number of the discretized epoch uncertainty levels.