Autoregressive spectral estimation in additive noise

The estimation of the spectral density of a discrete-time stationary Gaussian autoregressive process AR (p) from a finite set of noise observations is considered. A modified spectral estimator based on the high-order Yule-Walker equations is considered. Joint asymptotic normality of this spectral es...

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Bibliographic Details
Published in:IEEE transactions on acoustics, speech, and signal processing speech, and signal processing, 1988-04, Vol.36 (4), p.490-501
Main Authors: Gingras, D.F., Masry, E.
Format: Article
Language:English
Subjects:
Additive noise
Autoregressive processes
Covariance matrix
Equations
Gaussian noise
H infinity control
Limiting
Parameter estimation
Signal processing
Signal to noise ratio
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Summary:The estimation of the spectral density of a discrete-time stationary Gaussian autoregressive process AR (p) from a finite set of noise observations is considered. A modified spectral estimator based on the high-order Yule-Walker equations is considered. Joint asymptotic normality of this spectral estimator is established; a precise asymptotic expression for the covariance matrix of the limiting distribution is obtained. The special case of AR(1) plus noise is considered in some detail.< >
ISSN:0096-3518
DOI:10.1109/29.1553