Time-frequency-moving-average processes: principles and cepstral methods for parameter estimation

We introduce the time-frequency-moving-average (TFMA) model as a highly parsimonious time-varying MA model formulated in terms of time-frequency (TF) shifts. For estimation of the TFMA model parameters, we develop a computationally efficient nonlinear technique based on a novel complex TF cepstrum, TF cepstral recursions, and an underspread approximation. Simulation results demonstrate significant performance advantages of the proposed TFMA model and parameter estimation technique over an existing method for time-varying MA modeling and estimation.