Estimation of structured covariance matrices and multiple window spectrum analysis

Author

Van Veen, Barry D. ; Scharf, Louis L.

Author_Institution

Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA

Volume

38

Issue

8

fYear

1990

fDate

8/1/1990 12:00:00 AM

Firstpage

1467

Lastpage

1472

Abstract

A close relationship between low rank modeling and multiple window spectrum estimation is demonstrated by using maximum likelihood estimates of structured covariance matrices. The power in a narrow spectral band is estimated by estimating the variances in a low rank signal plus noise covariance model. This model is swept through the entire frequency band to obtain an estimate of power as a function of frequency. The resulting spectrum estimates are given by weighted combinations of eigenspectra. Each eigenspectrum results from projecting the data onto an orthogonal component of the signal subspace and squaring. The multiple window spectrum estimates of Thomson (1982) correspond to a particular choice for the low rank signal model. The low rank modeling and structured covariance matrix framework is also used to derive the maximum likelihood estimate for the center frequency of a signal in noise. This estimate is also obtained from a weighted combination of eigenspectra

Keywords

estimation theory; matrix algebra; spectral analysis; center frequency; eigenspectra; frequency band; low rank modeling; low rank signal; maximum likelihood estimates; multiple window spectrum analysis; noise; noise covariance model; orthogonal component; signal subspace; spectral band power; spectrum estimates; structured covariance matrices; Covariance matrix; Equations; H infinity control; Maximum likelihood estimation; Recursive estimation; Reflection; Signal processing algorithms; Spectral analysis; Speech processing; Stability;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.57585

Filename

57585