Adaptive noise subspace construction for harmonic retrieval

Christopher D. Schmitz, W. Kenneth Jenkins

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

Abstract

Thompson [1] showed that the eigenvector analysis required of Pisarenko's method of harmonic retrieval can be achieved on-line without explicit eigen decomposition. His method utilizes a unit-norm constrained adaptive filter to find and track a single vector that lies in the noise subspace. By tracking this vector without explicit formulation of the sample covariance matrix R or its eigen decomposition, the algorithm maintains a low computational cost. In this paper, Thompson's method is extended using a penalty method. The new algorithm seeks an orthonormal basis that spans the noise subspace. The computational complexity of the algorithm is then reduced to a more desirable level through the use of a relaxation technique. Once the noise subspace is constructed, one can compute the MUSIC power spectrum [2] for an improved spectral estimate.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
PublisherIEEE
PagesIII-21 - III-24
ISBN (Print)0780354710
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS '99 - Orlando, FL, USA
Duration: May 30 1999Jun 2 1999

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume3
ISSN (Print)0271-4310

Other

OtherProceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS '99
CityOrlando, FL, USA
Period5/30/996/2/99

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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