Two dimensional autoregressive estimation from noisy observations as a quadratic eigenvalue problem

Abstract

We consider the problem of two dimensional (2-D) autoregressive (AR) parameter estimation in the presence of observation noise. The proposed method is based on Yule-Walker Equations. We express the Yule Walker equations as a quadratic eigenvalue problem then by solving these equations, the parameters of the signal and noise are estimated. We also apply the proposed method to (2-D) spectrum estimation of the sinusoidal signals in noise. The performance of the proposed method is evaluated by computer simulation examples.

Adaptive Algorithm for Estimation of Two-Dimensional Autoregressive Fields from Noisy Observations

“…In this method, the driving noise process is forced to be non-Gaussian. Recently, in [16], combinations of the low order and high order Yule-Walker equations are solved as a quadratic eigenvalue problem. This method is an extension of the method proposed by Davila in [11].…”