Adaptive identification of sparse systems using the slim approach

Author

Glentis, G.-O.

Author_Institution

Dept. of Inf. & Telecommun., Univ. of Peloponnese, Tripoli, Greece

fYear

2014

fDate

1-5 Sept. 2014

Firstpage

760

Lastpage

764

Abstract

In this paper, a novel time recursive implementation of the Sparse Learning via Iterative Minimization (SLIM) algorithm is proposed, in the context of adaptive system identification. The proposed scheme exhibits fast convergence and tracking ability at an affordable computational cost. Numerical simulations illustrate the achieved performance gain in comparison to other existing adaptive sparse system identification techniques.

Keywords

adaptive signal processing; compressed sensing; identification; iterative methods; learning (artificial intelligence); minimisation; SLIM approach; adaptive sparse system identification techniques; compressing sensing; computational cost; fast convergence; numerical simulations; sparse learning via iterative minimization algorithm; time recursive algorithm; tracking ability; Adaptive systems; Algorithm design and analysis; Context; Convergence; Radio frequency; Signal processing; Signal processing algorithms; Adaptive system identification; SLIM algorithm; Sparse systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European

Conference_Location

Lisbon

Type

conf

Filename

6952231

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=148557