WARP convergence in conjugate gradient wiener filters

Hongya Ge, Magnus Lundberg, Louis L. Scharf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

In this work, we present interesting case studies that lead to new and deeper results on fast convergence of reduced-rank conjugate gradient (RRCG) Wiener filters (WF), for applications in communications. and sensor array signal processing. We discover that for signal modes with a specially structured Gram matrix, which induces L groups of distinct eigenvalues in the data covariance matrix, a fast and predictable convergence, in at most L steps, can be achieved when the RRCG WF is used to detect, and/or to focus on, the desired signal mode. For such applications, given knowledge of the repeated eigenstructure of the Gram matrix of signal modes or of the measurement covariance matrix, a RRCG Wiener filter, of at most rank L, delivers the same performance as the full-rank Wiener filter. Typically L is much less than the rank of the Gram matrix.

Original languageEnglish (US)
Title of host publication2004 Sensor Array and Multichannel Signal Processing Workshop
Pages109-113
Number of pages5
StatePublished - 2004
Event2004 Sensor Array and Multichannel Signal Processing Workshop - Barcelona, Spain
Duration: Jul 18 2004Jul 21 2004

Publication series

Name2004 Sensor Array and Multichannel Signal Processing Workshop

Other

Other2004 Sensor Array and Multichannel Signal Processing Workshop
CountrySpain
CityBarcelona
Period7/18/047/21/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'WARP convergence in conjugate gradient wiener filters'. Together they form a unique fingerprint.

Cite this