• DocumentCode
    851890
  • Title

    Estimation of multiple sinusoidal frequencies using truncated least squares methods

  • Author

    Hsieh, S.F. ; Liu, K.J.R. ; Yao, K.

  • Author_Institution
    Dept. of Commun. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • Volume
    41
  • Issue
    2
  • fYear
    1993
  • fDate
    2/1/1993 12:00:00 AM
  • Firstpage
    990
  • Lastpage
    994
  • Abstract
    To reduce the computational complexity of resolving closely spaced frequencies, three truncated QR methods are proposed: (1) truncated QR without column pivoting (TQR): (2) truncated QR with reordered columns) TQRR); and (3) truncated QR with column pivoting (TQRP). It is demonstrated that many of the benefits of the singular value decomposition based methods are achievable under the truncated QR methods with much lower computational cost. Based on the forward-backward linear prediction model, computer simulations and comparisons are provided for different truncation methods under various SNRs. Comparisons of asymptotic performance for large data samples are also given
  • Keywords
    estimation theory; filtering and prediction theory; least squares approximations; matrix algebra; signal processing; SNR; SVD; TQR; TQRP; TQRR; asymptotic performance; column pivoting; computational complexity; computer simulations; forward-backward linear prediction model; frequency estimation; large data samples; multiple sinusoidal frequencies; reordered columns; singular value decomposition; truncated QR; truncated least squares methods; Acoustic reflection; Array signal processing; Computational complexity; Computational efficiency; Computer simulation; Entropy; Frequency estimation; Least squares methods; Maximum likelihood estimation; Optical reflection; Predictive models; Signal processing; Signal processing algorithms; Singular value decomposition; Speech processing;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.193242
  • Filename
    193242