Causal cyclic Wiener filtering

We develop a causal filter bank implementation of the cyclic Wiener filter for periodically correlated (PC, or cyclostationary) time series. By converting the PC time series into a vector-valued wide-sense stationary (WSS) time series, we may utilize the existing literature on factorization of spectral density matrices. However, because PC analytic and equivalent baseband signals are generally improper, spectral factorization algorithms must be modified for the improper case. Then, given the spectral density matrix for the equivalent WSS vector process, a causal cyclic Wiener filter can be implemented as a multirate filter bank or an equivalent polyphase structure.