Adaptive noise subspace construction for harmonic retrieval

Christopher D. Schmitz, William 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
    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

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

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