• DocumentCode
    1134205
  • Title

    Givens rotation based least squares lattice and related algorithms

  • Author

    Ling, Fuyun

  • Author_Institution
    Codex Corp., Mansfield, MA, USA
  • Volume
    39
  • Issue
    7
  • fYear
    1991
  • fDate
    7/1/1991 12:00:00 AM
  • Firstpage
    1541
  • Lastpage
    1551
  • Abstract
    The author presents a general and systematic approach for deriving new LS (least squares) estimation algorithms that are based solely on Givens rotations. In particular, this approach is used to derive efficient Givens-rotation-based LS lattice algorithms-the Givens-lattice algorithms. By exploiting the relationship between the Givens algorithms and the recursive modified Gram-Schmidt algorithm, it is shown that the time and order update of any order-recursive LS estimation algorithm can be realized by employing only Givens rotations. Applying this general conclusion to LS estimation of time-series signals results in the Givens-lattice algorithms. Two Givens-lattice algorithms, one with square roots and the other without, are presented. It is shown that the Givens-lattice algorithms are computationally more efficient than the fast QR algorithm of Cioffi (1987). The derivation of other Givens rotation-based LS estimation algorithms and their systolic array implementations are discussed
  • Keywords
    least squares approximations; signal processing; Givens rotations; Givens-lattice algorithms; least squares lattice algorithm; order update; order-recursive LS estimation algorithm; recursive modified Gram-Schmidt algorithm; signal processing; square roots; systolic array; time update; time-series signals; Adaptive filters; Estimation error; Filtering algorithms; Lattices; Least squares approximation; Least squares methods; Numerical stability; Recursive estimation; Signal processing algorithms; Systolic arrays;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.134393
  • Filename
    134393