Signal estimation via selective harmonic amplification: MUSIC, redux

Authors
Citation
Tt. Georgiou, Signal estimation via selective harmonic amplification: MUSIC, redux, IEEE SIGNAL, 48(3), 2000, pp. 780-790
Citations number
24
Categorie Soggetti
Eletrical & Eletronics Engineeing
Journal title
IEEE TRANSACTIONS ON SIGNAL PROCESSING
ISSN journal
1053587X → ACNP
Volume
48
Issue
3
Year of publication
2000
Pages
780 - 790
Database
ISI
SICI code
1053-587X(200003)48:3<780:SEVSHA>2.0.ZU;2-9
Abstract
The technique known as multiple signal classification (MUSIC) is a semi-emp irical way to obtain pseudo-spectra that highlight the spectral-energy dist ribution of a time series. It Is based on a certain canonical decomposition of a Toeplitz matrix formed out of an estimated autocorrelation sequence. The purpose of this paper is to present an analogous canonical decompositio n of the state-covariance matrix of a stable linear filter filter by a give n time-series, Accordingly, the paper concludes with a modification of MUSI C. The new method starts with filtering the time series and then estimating the covariance of the state of the filter. This step in essence improves t he signal-to-noise ratio (SNR) by amplifying the contribution to the actual value of the state-covariance of a selected harmonic interval where spectr al lines are expected to reside. Then, the method capitalizes on the canoni cal decomposition of the filter state-covariance to retrieve information on the location of possible spectral lines. The framework requires uniformly spaced samples of the process.