Algorithms for improved performance in adaptive polynomial filters with Gaussian input signals

Xiaohui Li, William Kenneth Jenkins, Charles W. Therrien

    Research output: Contribution to journalConference article

    1 Citation (Scopus)

    Abstract

    The structure of the input covariance matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the covariance matrix inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the covariance matrix that results from this formulation.

    Original languageEnglish (US)
    Pages (from-to)267-270
    Number of pages4
    JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
    Volume1
    StatePublished - Jan 1 1997
    EventProceedings of the 1996 30th Asilomar Conference on Signals, Systems & Computers. Part 2 (of 2) - Pacific Grove, CA, USA
    Duration: Nov 3 1996Nov 6 1996

    Fingerprint

    Covariance matrix
    Polynomials
    Adaptive algorithms
    Adaptive filters

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Computer Networks and Communications

    Cite this

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    title = "Algorithms for improved performance in adaptive polynomial filters with Gaussian input signals",
    abstract = "The structure of the input covariance matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the covariance matrix inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the covariance matrix that results from this formulation.",
    author = "Xiaohui Li and Jenkins, {William Kenneth} and Therrien, {Charles W.}",
    year = "1997",
    month = "1",
    day = "1",
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    pages = "267--270",
    journal = "Conference Record of the Asilomar Conference on Signals, Systems and Computers",
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    Algorithms for improved performance in adaptive polynomial filters with Gaussian input signals. / Li, Xiaohui; Jenkins, William Kenneth; Therrien, Charles W.

    In: Conference Record of the Asilomar Conference on Signals, Systems and Computers, Vol. 1, 01.01.1997, p. 267-270.

    Research output: Contribution to journalConference article

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    T1 - Algorithms for improved performance in adaptive polynomial filters with Gaussian input signals

    AU - Li, Xiaohui

    AU - Jenkins, William Kenneth

    AU - Therrien, Charles W.

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    N2 - The structure of the input covariance matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the covariance matrix inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the covariance matrix that results from this formulation.

    AB - The structure of the input covariance matrix in Volterra second order adaptive filters for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efficient fast adaptive algorithm. It is shown that when the input signal samples are ordered properly within the input data vector, the covariance matrix inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms are presented that take advantage of the sparsity and unique structure of the covariance matrix that results from this formulation.

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    M3 - Conference article

    VL - 1

    SP - 267

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    JO - Conference Record of the Asilomar Conference on Signals, Systems and Computers

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