MAXIMUM-LIKELIHOOD DOA ESTIMATION AND ASYMPTOTIC CRAMER-RAO BOUNDS FOR ADDITIVE UNKNOWN COLORED NOISE

被引：100

作者：

YE, H ^{[1
]}

DEGROAT, RD ^{[1
]}

机构：

[1] UNIV TEXAS,FAC ELECT ENGN,ERIK JONSSON SCH ENGN & COMP SCI,RICHARDSON,TX 75083

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 1995年 / 43卷 / 04期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/78.376846

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 devoted to the maximum likelihood estimation of multiple sources in the presence of unknown noise, With the spatial noise covariance modeled as a function of certain unknown parameters, e.g., an autoregressive (AR) model, a direct and systematic way is developed to find the exact maximum likelihood (ML) estimates of all parameters associated with the direction finding problem, including the direction-of-arrival (DOA) angles Theta, the noise parameters alpha, the signal covariance Phi(s), and the noise power sigma(2). We show that the estimates of the linear part of the parameter set Phi(s) and sigma(2) can be separated from the nonlinear parts Theta and alpha. Thus, the estimates of Phi(s), and sigma(2) become explicit functions of Theta and alpha. This results in a significant reduction in the dimensionality of the nonlinear optimization problem. Asymptotic analysis is performed on the estimates of Theta and alpha, and compact formulas are obtained for the Cramer-Rao bounds (CRB's), Finally, a Newton-type algorithm is designed to solve the nonlinear optimization problem, and simulations show that the asymptotic CRB agrees well with the results from Monte Carlo trials, even for small numbers of snapshots.

引用

页码：938 / 949

页数：12

共 19 条

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