On the performance of the MLE of adaptive array weights: a comparison

Author

Lin, Shen-de ; Barkat, Mourad

Author_Institution

Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA

fYear

1989

fDate

1-2 June 1989

Firstpage

443

Lastpage

446

Abstract

The maximum-likelihood (ML) adaptive weight vector is derived, and its performance is compared with those of the sample matrix inversion (SMI) method and the least-mean-square (LMS) algorithm. The ML adaptive weight vector and the SMI method achieve identical convergence rates for the average SNR. With the desired signal absent, they are superior to the LMS algorithm. However, when the desired signal is present and the optimum SNR which depends on the received SNR is large, they lose their superiority. For the average MSE performance, the convergence of the ML adaptive weights is the fastest when the optimum SNR is high enough. With a small optimum SNR, the SMI method performs better than the other algorithms.<>

Keywords

convergence of numerical methods; signal processing; LMS algorithm; ML adaptive weight vector; MLE; MSE performance; SMI method; adaptive array weights; array processing; average SNR; convergence rates; least-mean-square; linear array; maximum likelihood estimation; sample matrix inversion; signal processing; Adaptive arrays; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Iterative algorithms; Least squares approximation; Maximum likelihood estimation; Sensor arrays; Signal processing algorithms; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Computers and Signal Processing, 1989. Conference Proceeding., IEEE Pacific Rim Conference on

Conference_Location

Victoria, BC, Canada

Type

conf

DOI

10.1109/PACRIM.1989.48397

Filename

48397