Joint Detection of Almost-Cyclostationary Signals and Estimation of Their Cycle Period

We propose a technique that jointly detects the presence of almost-cyclostationary (ACS) signals in wide-sense stationary noise and provides an estimate of their cycle period. Since the cycle period of an ACS process is not an integer, the approach is based on a combination of a resampling stage and a multiple hypothesis test, which deal separately with the fractional part and the integer part of the cycle period. The approach requires resampling the signal at many different rates, which is computationally expensive. For this reason, we propose a filter bank structure that allows us to efficiently resample a signal at many different rates by identifying common interpolation stages among the set of resampling rates.