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Low complexity adaptive algorithm for generalized eigenvalue decomposition

  • Rong Wang
  • , Feifei Gao
  • , Minli Yao
  • , Hongxing Zou
  • Xi'an Research Institute of High Technology
  • Tsinghua University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

It is well known that the generalized eigenvalue decomposition (GEVD) can be used in a number of signal processing applications, for example, subspace tracking and estimation in the presence of colored noise. In this paper, we propose a new approach to extract the principle generalized eigenvectors (PGEs) for GEVD. Resorting to a weighted non-quadratic criterion (WNQC), the designed algorithm has a steep land-scape, such that the desired point can be obtained from fast gradient-based method. Applying the projection approximation and recursive least squares (RLS) technique, we develop an adaptive algorithm with low computational complexity to parallelly estimate the PGEs. Finally, numerical results are provided to demonstrate the effectiveness of the proposed studies.

Original languageEnglish
Title of host publication2013 8th International ICST Conference on Communications and Networking in China, CHINACOM 2013 - Proceedings
Pages690-693
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 8th International ICST Conference on Communications and Networking in China, CHINACOM 2013 - Guilin, China
Duration: 14 Aug 201316 Aug 2013

Publication series

Name2013 8th International ICST Conference on Communications and Networking in China, CHINACOM 2013 - Proceedings

Conference

Conference2013 8th International ICST Conference on Communications and Networking in China, CHINACOM 2013
Country/TerritoryChina
CityGuilin
Period14/08/1316/08/13

Keywords

  • colored noise
  • Generalized eigenvalue decomposition
  • weighted nonquadratic criterion

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