Cyclic-cumulant based identification of almost periodically time-varying systems: parametric methods

Identification of known order (almost) periodically time-varying autoregressive moving average (ARMA) models is addressed using cyclic cumulants of output only, or, input/output data. Several approaches are presented under different modeling assumptions and their relative merits are discussed. Both linear and nonlinear algorithms are derived in time- and cyclic-domains and optimality issues are considered. All the algorithms utilize consistent single record estimators, are phase sensitive and are shown to be insensitive to any stationary noise as well as additive (perhaps nonstationary) Gaussian noise of unknown covariance