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

Xiaohui Li, W. Kenneth Jenkins, Charles W. Therrien

    Research output: Contribution to journalConference articlepeer-review

    1 Scopus citations


    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
    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

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Computer Networks and Communications

    Cite this