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On the Detection of Gaussian Cyclostationary Random Processes

This paper extends previous work on the likelihood detection of cyclostationary processes in stationary Gaussian noise. In contrast to previous developments, we use a Gaussian cyclostationary signal assumption rather than a weak signal assumption. Under the assumption of completely known statistics for signal and noise, the likelihood ratio detector is derived for two related cases: signal detection and detection of cyclostationarity. The difference between these two cases involves different models for the stationary statistics under the two hypotheses.