Frequency estimation by principal component AR spectral estimation method without eigendecomposition

Author

Kay, Steven M. ; Shaw, Arnab K.

Author_Institution

Dept. of Electr. Eng., Rhode Island Univ., Kingston, RI, USA

Volume

36

Issue

1

fYear

1988

fDate

1/1/1988 12:00:00 AM

Firstpage

95

Lastpage

101

Abstract

An eigenvalue filtering method is proposed that applies a transformation to an autocorrelation matrix, which has the effect of truncating the undesired eigenvalues so that the corresponding matrix function closely approximates the pseudoinverse. It is shown using a computer simulation that compared to the forward-backward method, the proposed method enhances the threshold in SNR by about 6-8 dB. Further improvement is obtained by a simple subset selection method and a second eigenvalue filtering iteration

Keywords

correlation methods; digital simulation; eigenvalues and eigenfunctions; electrical engineering computing; filtering and prediction theory; spectral analysis; autocorrelation matrix; autoregressive spectral estimation; computer simulation; eigenvalue filtering method; frequency estimation; matrix function; pseudoinverse; subset selection method; transformation; Autocorrelation; Computer simulation; Eigenvalues and eigenfunctions; Filtering; Frequency estimation; Geophysical signal processing; Matrix decomposition; Signal processing; Signal to noise ratio; White noise;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.1492

Filename

1492