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

    Fingerprint

    Decomposition
    Adaptive filters
    Covariance matrix
    Power spectrum
    Eigenvalues and eigenfunctions
    Computational complexity
    Costs

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

    Schmitz, C. D., & Jenkins, W. K. (1999). Adaptive noise subspace construction for harmonic retrieval. In Proceedings - IEEE International Symposium on Circuits and Systems (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 3). IEEE.
    Schmitz, Christopher D. ; Jenkins, William Kenneth. / Adaptive noise subspace construction for harmonic retrieval. Proceedings - IEEE International Symposium on Circuits and Systems. IEEE, 1999. (Proceedings - IEEE International Symposium on Circuits and Systems).
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    title = "Adaptive noise subspace construction for harmonic retrieval",
    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.",
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    Schmitz, CD & Jenkins, WK 1999, Adaptive noise subspace construction for harmonic retrieval. in Proceedings - IEEE International Symposium on Circuits and Systems. Proceedings - IEEE International Symposium on Circuits and Systems, vol. 3, IEEE, Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, ISCAS '99, Orlando, FL, USA, 5/30/99.

    Adaptive noise subspace construction for harmonic retrieval. / Schmitz, Christopher D.; Jenkins, William Kenneth.

    Proceedings - IEEE International Symposium on Circuits and Systems. IEEE, 1999. (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 3).

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

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    AB - 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.

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    Schmitz CD, Jenkins WK. Adaptive noise subspace construction for harmonic retrieval. In Proceedings - IEEE International Symposium on Circuits and Systems. IEEE. 1999. (Proceedings - IEEE International Symposium on Circuits and Systems).