A new iterative weighted norm minimization algorithm and its applications

Author

Gorodnitsky, Irina F. ; Rao, Bhaskar D.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA

fYear

1992

fDate

7-9 Oct 1992

Firstpage

412

Lastpage

415

Abstract

A general class of linear inverse problems in which the solutions are sparse and localized is considered. The proposed algorithm is a nonparametric approach that finds sparse and localized solutions without prior information on the constraints. Each step of the iterative procedure consists in solving a weighted least squares problem wherein the weighting matrix is determined by the solution from the previous iteration. Some properties of the algorithm along with its applications to problems in direction of arrival and spectrum estimation are presented

Keywords

inverse problems; iterative methods; least squares approximations; minimisation; nonparametric statistics; signal processing; DOA estimation; constraints; direction of arrival; iterative weighted norm minimization algorithm; linear inverse problems; nonparametric approach; sparse and localized solutions; spectrum estimation; weighted least squares problem; Application software; Direction of arrival estimation; Frequency; Inverse problems; Iterative algorithms; Least squares methods; Minimization methods; Sampling methods; Signal processing algorithms; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on

Conference_Location

Victoria, BC

Print_ISBN

0-7803-0508-6

Type

conf

DOI

10.1109/SSAP.1992.246872

Filename

246872