Engineering Commons^™

Estimation Of Autoregressive Parameters From Noisy Observations Using Iterated Covariance Updates, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with …