Next Article in Journal
Improving Underwater Continuous-Variable Measurement-Device-Independent Quantum Key Distribution via Zero-Photon Catalysis
Previous Article in Journal
Systems with Size and Energy Polydispersity: From Glasses to Mosaic Crystals
Open AccessArticle

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Electrical and Computer Engineering Department, Utah State University, Logan, UT 84332, USA
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 572; https://doi.org/10.3390/e22050572
Received: 16 April 2020 / Revised: 11 May 2020 / Accepted: 16 May 2020 / Published: 19 May 2020
(This article belongs to the Section Information Theory, Probability and Statistics)
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 the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation. View Full-Text
Keywords: autoregressive model estimation; spectrum estimation; vector AR model; RLS algorithm autoregressive model estimation; spectrum estimation; vector AR model; RLS algorithm
Show Figures

Figure 1

MDPI and ACS Style

Moon, T.K.; Gunther, J.H. Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates. Entropy 2020, 22, 572.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop