Asynchronous classification of MFSK signals using the higher order correlation domain

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 sensitivity gaps in performance incurred by the synchronous rules when the unknown signal time of arrival or epoch offsets are introduced. This sensitivity is ameliorated by averaging over a reduced-uncertainty epoch model. Fairly satisfactory results are reported with a small number of the discretized epoch uncertainty levels.