Estimation of the parameters of autoregressive signals from colored noise-corrupted measurements

This paper is concerned with identification of autoregressive (AR) model pa rameters using observations corrupted with colored noise. A novel formulati on of an auxiliary least-squares estimator is introduced so that the autoco variance functions of the colored observation noise can be estimated in a s traightforward manner. With this, the colored-noise-induced estimation bias can be removed to yield the unbiased estimate of the AR parameters. The pe rformance of the proposed unbiased estimation algorithm is illustrated by s imulation results. The presented work greatly extends the author's previous methods that were developed for identification of AR signals observed in w hite noise.