Maximum likelihood parameter estimation of noisy data

For most signal models of interest, Maximum Likelihood (ML) parameter estimation in the presence of noise is a difficult, non-linear problem. A new iterative algorithm has been developed for ML estimation, however, which effectively decouples the uncertainty in the signal and parameter values, thus simplifying the calculation required. It can be shown that the likelihood function increases on each iteration of the algorithm. When applied to a particular pole-zero (ARMA) signal model, each pass consists of a linear smoothing filter followed by solving a set of linear equations for both the pole and zero polynomial coefficients.