A New Multi-Algorithm Approach to Sparse System Adaptation

Author

Ashrith Deshpande
Steven L. Grant, Missouri University of Science and TechnologyFollow

Abstract

This paper introduces a new combination of adaptive algorithms for the identification of sparse systems. Two similar adaptive filters, proportionate normalized least mean squares (PNLMS) and exponential gradient (EG) have been shown to have initial convergence that is much faster than the classical normalized least mean squares (NLMS) when the system to be identified is sparse. Unfortunately, after the initial phase, the convergence is then actually slower than NLMS. Another algorithm developed by Gansler, Benesty, Sondhi, and Gay, which we will refer to as GBSG, operates in a manner complementary to PNLMS and EG. its initial convergence is at about the same rate as NLMS, but gradually accelerates to a fast final convergence. by combining both algorithms, PNLMS and GBSG we obtain fast adaptation convergence rates in both initial and final phases of the process.

Recommended Citation

A. Deshpande and S. L. Grant, "A New Multi-Algorithm Approach to Sparse System Adaptation," 13th European Signal Processing Conference, EUSIPCO 2005, pp. 1910 - 1913, Institute of Electrical and Electronics Engineers, Dec 2005.

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

978-160423821-1

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Dec 2005