Title :
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
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;
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
DOI :
10.1109/SSAP.1992.246872
