Improving threshold performance of the IQML algorithm

Author

Read, William J L

Author_Institution

Dept. of Nat. Defence, Defence Res. Establ., Ottawa, Ont., Canada

Volume

48

Issue

9

fYear

2000

fDate

9/1/2000 12:00:00 AM

Firstpage

2662

Lastpage

2665

Abstract

In this article, improvements to the iterative quadratic maximum likelihood (IQML) algorithm are presented. The modifications are twofold: modification of the nontriviality constraint on the polynomial coefficient vector and the addition of a noise preprocessing step based on the eigendecomposition of the data covariance matrix. Simulation results are provided

Keywords

Gaussian noise; array signal processing; covariance matrices; direction-of-arrival estimation; iterative methods; matrix decomposition; maximum likelihood estimation; polynomials; DOA estimation; IQML algorithm; additive Gaussian noise; array signal processing; data covariance matrix; direction-of-arrival estimation; eigendecomposition; iterative quadratic maximum likelihood algorithm; maximum likelihood estimation; noise preprocessing; polynomial coefficient vector modification; simulation results; threshold performance improvement; uniform linear array; Covariance matrix; Frequency estimation; Frequency measurement; Iterative algorithms; Maximum likelihood estimation; Noise measurement; Polynomials; Sensor arrays; Signal processing algorithms; Time measurement;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.863073

Filename

863073