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
Link To Document :
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