Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies

Author

Stoica, Petre ; Söderström, Torsten

Author_Institution

Dept. of Autom. Control, Polytech. Inst. of Bucharest, Romania

Volume

39

Issue

8

fYear

1991

fDate

8/1/1991 12:00:00 AM

Firstpage

1836

Lastpage

1847

Abstract

Consideration is given to the analysis of the large-sample second-order properties of multiple signal classification (MUSIC) and subspace rotation (SUR) methods, such as ESPRIT, for sinusoidal frequency estimation. Explicit expressions for the covariance elements of the estimation errors associated with either method are derived. These expressions of covariances are then used to analyze and compare the statistical performances of the MUSIC and SUR estimation (SURE) methods. Both MUSIC and SURE are based on the eigendecomposition of a sample data covariance matrix. The expressions for the estimation error variances derived are used to study the dependence of MUSIC and SURE performances on the dimension of the data covariance matrix used

Keywords

parameter estimation; signal processing; statistical analysis; ESPRIT; MUSIC; SURE; data covariance matrix; eigendecomposition; estimation errors; multiple signal classification; sinusoidal frequency estimation; statistical analysis; subspace rotation; Array signal processing; Covariance matrix; Estimation error; Frequency estimation; Helium; Multiple signal classification; Performance analysis; Sensor arrays; Signal analysis; Statistical analysis;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.91154

Filename

91154