DocumentCode :
894757
Title :
A QR-type algorithm for fitting the delta AR model to autocorrelation windowed data
Author :
Zarowski, Christopher J.
Author_Institution :
Dept. of Electr. Eng., Queen´´s Univ., Kingston, Ont., Canada
Volume :
41
Issue :
4
fYear :
1993
fDate :
4/1/1993 12:00:00 AM
Firstpage :
1728
Lastpage :
1730
Abstract :
A QR algorithm is developed to fit the delta autoregressive (DAR) model of Vijayan et al. (see IEEE Trans. Automat. Cont. vol.36, p.314, 1991) to autocorrelation windowed sampled data. To obtain the DAR model parameters, one must solve a linear system in the matrix called Qn. Unfortunately, there is presently no way to obtain good estimates of Qn. The proposed QR-type algorithm overcomes this problem by computing the DAR model parameters without the need for estimating Qn directly. The QR algorithm proposed is a simple modification of the classical QR algorithm for the conventional AR model due to C.P. Rialan and L.L. Scharf (1986)
Keywords :
correlation theory; matrix algebra; signal processing; QR algorithm; autocorrelation windowed data; delta AR model; linear system; matrix; model parameters; sampled data; Autocorrelation; Filtering; Kalman filters; Linear systems; Riccati equations; Sampling methods; Signal processing algorithms; State feedback;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.212757
Filename :
212757
Link To Document :
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