MUSIC, MAXIMUM-LIKELIHOOD, AND CRAMER-RAO BOUND - FURTHER RESULTS AND COMPARISONS

被引:380
作者
STOICA, P [1 ]
NEHORAI, A [1 ]
机构
[1] YALE UNIV,DEPT ELECT ENGN,NEW HAVEN,CT 06520
来源
IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING | 1990年 / 38卷 / 12期
关键词
D O I
10.1109/29.61541
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
The problem of determining the direction of arrival of narrow-band plane waves using sensor arrays and the related one of estimating the parameters of superimposed signals from noisy measurements have received significant attention in the recent signal processing literature. A number of results have been presented recently in an article by the authors of this paper on the statistical performance of the multiple signal characterization (MUSIC) and the maximum likelihood (ML) estimators for the above problems. This companion paper extends the results of our previous article in several directions. First, it establishes that in the class of weighted MUSIC estimators, the unweighted MUSIC achieves the best performance (i.e., the minimum variance of estimation errors), in large samples. Next, it derives the covariance matrix of the ML estimator and presents detailed analytic studies of the statistical efficiency of MUSIC and ML estimators. These studies include performance comparisons of MUSIC and MLE with each other as well as with the ultimate performance corresponding to the Cramér-Rao bound (CRB). Finally, the paper contains some numerical examples which provide a more quantitative study of performance for the problem of finding two directions with uniform linear sensor arrays. © 1990 IEEE
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收藏
页码:2140 / 2150
页数:11
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