STATISTICAL-ANALYSIS OF MUSIC AND SUBSPACE ROTATION ESTIMATES OF SINUSOIDAL FREQUENCIES

被引:138
作者
STOICA, P [1 ]
SODERSTROM, T [1 ]
机构
[1] UNIV UPPSALA,DEPT TECHNOL,AUTOMAT CONTROL & SYST ANAL GRP,S-75103 UPPSALA,SWEDEN
关键词
D O I
10.1109/78.91154
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper is concerned with 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 first derived. These expressions of covariances are then used to analyze and compare the statistical performances of 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 in this paper are also used to study the dependence of MUSIC and SURE performances on the dimension or the data covariance matrix employed.
引用
收藏
页码:1836 / 1847
页数:12
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