Data modeling through robust nonlinear least squares

Author

Yardimci, Yasemin ; Cadzow, James A. ; Çetin, A. Enis

Author_Institution

Dept. of Electr. Eng., Vanderbilt Univ., Nashville, TN, USA

fYear

1994

fDate

12-14 Apr 1994

Firstpage

88

Abstract

A nonlinear least-squares (LS) method for modeling empirically obtained data in sensor array signal processing is developed. The new method is robust with respect to outliers and has a comparable computational complexity to the standard least-squares method. Robustness is achieved by introducing a nonlinear function which weights the squared error term in the LS criterion. Weighting functions for mixture of two Gaussian distributions are determined by maximum likelihood estimation theory. The strength of this algorithm is demonstrated by simulation examples for the direction-of-arrival (DOA) estimation problem

Keywords

Gaussian distribution; direction-of-arrival estimation; least mean squares methods; maximum likelihood estimation; modelling; DOA; Gaussian distributions; computational complexity; data modeling; direction-of-arrival estimation; maximum likelihood estimation theory; nonlinear function; parameter estimation; robust nonlinear least squares; sensor array signal processing; simulation; squared error weighting; weighting functions; Array signal processing; Computational complexity; Computational modeling; Direction of arrival estimation; Gaussian distribution; Least squares methods; Maximum likelihood estimation; Robustness; Sensor arrays; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference, 1994. Proceedings., 7th Mediterranean

Conference_Location

Antalya

Print_ISBN

0-7803-1772-6

Type

conf

DOI

10.1109/MELCON.1994.381137

Filename

381137