Convergence analysis of a class of adaptive weighted norm extrapolation algorithms

Author

Gorodnitsky, Irina F. ; Rao, Bhaskar D.

Author_Institution

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

fYear

1993

fDate

1-3 Nov 1993

Firstpage

339

Abstract

Adaptive weighted norm extrapolation algorithms can provide superior performance for estimation of sparse signals from limited data. We present theoretical analysis results for a class of these algorithms that include a proof of the global convergence, the rate of convergence derivation, and characterization of the fixed points. We also propose a general class of adaptive weighted extrapolation algorithms and introduce a more general problem formulation which greatly expands the range of applications of the algorithm

Keywords

adaptive signal processing; convergence of numerical methods; extrapolation; adaptive weighted norm extrapolation algorithms; convergence analysis; convergence derivation rate; fixed points characterization; global convergence; limited data; sparse signals estimation; Adaptive signal processing; Algorithm design and analysis; Convergence; Direction of arrival estimation; Extrapolation; Interpolation; Pattern recognition; Signal processing algorithms; Signal resolution; Tomography;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-4120-7

Type

conf

DOI

10.1109/ACSSC.1993.342530

Filename

342530