SEARCH-EFFICIENT METHODS OF DETECTION OF CYCLOSTATIONARY SIGNALS

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-sig nal detection and broadband search, problems in implementation, such a s excessive computational complexity and storage and search arise. Thi s paper introduces two new search-efficient methods of cycle detection , namely the autocorrelated cyclic autocorrelation (ACA) and the autoc orrelated cyclic periodogram (ACP) methods. For a given level of perfo rmance reliability, the ACA and ACP methods allow much larger resoluti on width in cycle frequency to be used in their implementations, compa red to the conventional methods of cyclic spectral analysis. Thus, the amount of storage and search can be substantially reduced. Analyses o f the two methods, performance comparison, and computer simulation res ults are presented.