Time-warped almost-cyclostationary signals: Characterization and statistical function measurements

Time-warped (TW) almost-cyclostationary (ACS) processes are obtained from ACS processes by linear time-variant transformations that modify the time scale. TW-ACS processes are shown to be a subclass of the oscillatory ACS processes, a recently introduced class of signals extending that of the ACS ones. The TW-ACS model is useful to describe signals with hidden imperfect periodicities. An example is the received signal when the transmitted one is ACS in the case of general motion law between transmitter and receiver. Some biological signals are other examples. The problem of statistical function estimation is addressed and two estimation techniques are proposed. The theoretical results are corroborated by numerical experiments on simulated and real data.