Cyclostationarity: half a century of research
Signal Processing
Bibliography on cyclostationarity
Signal Processing
Automatic modulation classification for cognitive radios using cyclic feature detection
IEEE Circuits and Systems Magazine
Component statistical analysis of second order hidden periodicities
Digital Signal Processing
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Conventional signal processing methods that exploit cyclostationarity for the detection of weak signals in noise require fine resolution in cycle frequency for long integration time. Hence, in cases of weak-signal detection and broadband search, problems in implementation, such as excessive computational complexity and storage and search arise. This paper introduces two new search-efficient methods of cycle detection, namely the autocorrelated cyclic autocorrelation (ACA) and the autocorrelated cyclic periodogram (ACP) methods. For a given level of performance reliability, the ACA and ACP methods allow much larger resolution width in cycle frequency to be used in their implementations, compared to the conventional methods of cyclic spectral analysis. Thus, the amount of storage and search can be substantially reduced. Analyses of the two methods, performance comparison, and computer simulation results are presented