An asymptotic GLRT for the detection of cyclostationary signals

摘要: We derive the generalized likelihood ratio test (GLRT) for detecting cyclostationarity in scalar-valued time series. The main idea behind our approach is Gladyshev's relationship, which states that when cyclostationary signal blocked at known cycle period it produces a vector-valued wide-sense stationary process. This result amounts to saying covariance matrix of vector obtained by stacking all observations series block-Toeplitz if cyclostationary, and Toeplitz stationary. derivation GLRT requires maximum estimates matrices. can be managed asymptotically (for large number samples) exploiting Szego's theorem its generalization processes. Simulation results show good performance proposed GLRT.