DocumentCode
1302853
Title
A subspace approach to estimation of autoregressive parameters from noisy measurements
Author
Davila, Carlos E.
Author_Institution
Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA
Volume
46
Issue
2
fYear
1998
fDate
2/1/1998 12:00:00 AM
Firstpage
531
Lastpage
534
Abstract
This correspondence describes a method for estimating the parameters of an autoregressive (AR) process from a finite number of noisy measurements. The method uses a modified set of Yule-Walker (YW) equations that lead to a quadratic eigenvalue problem that, when solved, gives estimates of the AR parameters and the measurement noise variance
Keywords
autoregressive processes; eigenvalues and eigenfunctions; parameter estimation; random noise; random processes; spectral analysis; AR process; Yule-Walker equations; autoregressive process; measurement noise variance; noisy measurements; parameter estimation; quadratic eigenvalue problem; subspace approach; Additive noise; Autocorrelation; Biomedical measurements; Eigenvalues and eigenfunctions; Equations; Linear predictive coding; Noise measurement; Parameter estimation; Random processes; White noise;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/78.655442
Filename
655442
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