Nonstationary signal estimation using time-varying ARMA models

A parametric approach for the estimation of nonstationary signals is presented. The approach is based on time-varying autoregressive moving average (TARMA) signal representations. The TARMA model coefficients vary in a deterministically organized way and are estimated using a novel fully linear parameter estimation method. The estimation algorithm is based on the properties of the TARMA models that allow their manipulations using operations restricted to the time domain. It is shown that the estimation method is computationally simple, overcomes local extrema problems associated with nonlinear search procedures, and eliminates the need for initial guesses of the parameter values