• DocumentCode
    1537652
  • Title

    Computing the reduced rank Wiener filter by IQMD

  • Author

    Hua, Yingbo ; Nikpour, Maziar

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
  • Volume
    6
  • Issue
    9
  • fYear
    1999
  • Firstpage
    240
  • Lastpage
    242
  • Abstract
    The reduced-rank Wiener filter (RRWF) is a generic tool for data compression and filtering. This letter presents an iterative quadratic minimum distance (IQMD) algorithm for computing the RRWF. Although it is iterative in nature, the IQMD algorithm is shown to be globally and exponentially convergent under some weak conditions. While the conventional algorithms for computing the RRWF require an order of n/sup 3/ flops, the IQMD algorithm requires only an order of n/sup 2/ flops at each iteration where n is the dimension of data. The number of iterations required in practice is often small due to the exponential convergence rate of the IQMD.
  • Keywords
    Karhunen-Loeve transforms; Wiener filters; convergence of numerical methods; data compression; filtering theory; iterative methods; IQMD algorithm; Karhunen-Loeve transform; data compression; exponential convergence; filtering; generic tool; global convergence; iterative quadratic minimum distance algorithm; reduced rank Wiener filter; Closed-form solution; Convergence; Data compression; Filtering; Iterative algorithms; Karhunen-Loeve transforms; Signal processing; Singular value decomposition; Wiener filter;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/97.782070
  • Filename
    782070