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dc.contributor.authorNowak, Robert David
Van Veen, Barry D.
dc.creatorNowak, Robert David
Van Veen, Barry D.
dc.date.accessioned 2007-10-31T00:56:11Z
dc.date.available 2007-10-31T00:56:11Z
dc.date.issued 1994
dc.date.submitted 2004-01-13
dc.identifier.citation R. D. Nowak and B. D. Van Veen, "Efficient Methods for Identification of Volterra Filters," Signal Processing, 1994.
dc.identifier.urihttps://hdl.handle.net/1911/20156
dc.description Journal Paper
dc.description.abstract A major drawback of the truncated Volterra series or "Volterra filter" for system identification is the large number of parameters required by the standard filter structure. The corresponding estimation problem requires the solution of a large system of simultaneous linear equations. Two methods for simplifying the estimation problem are discussed in this paper. First, a Kronecker product structure for the Volterra filter is reviewed. In this approach the inverse of the large correlation matrix is expressed as a Kronecker product of small matrices. Second, a parallel decomposition of the Volterra filter based on uncorrelated, symmetric inputs is introduced. Here the Volterra filter is decomposed into a parallel combination of smaller orthogonal "sub-filters." It is shown that each sub-filter is much smaller than the full Volterra filter and hence the parallel decomposition offers many advantages for estimating the Volterra kernels. Simulations illustrate application of the parallel structure with random and pseudorandom excitations. Input conditions that guarantee the existence of a unique estimate are also reviewed.
dc.description.sponsorship Army Research Office
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjectTemporary
dc.subject.otherWavelet based Signal/Image Processing
dc.title Efficient Methods for Identification of Volterra Filters
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle Signal Processing
dc.date.modified 2004-11-05
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordTemporary
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1016/0165-1684(94)90157-0


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    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1289]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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