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
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