NDLI: Accurate estimation of AR model by tapered SVD without rank determination

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Accurate estimation of AR model by tapered SVD without rank determination

Content Provider	IEEE Xplore Digital Library
Author	Kanai, H. Chubachi, N.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Eng., Tohoku Univ., Sendai, Japan (Kanai, H.; Chubachi, N.)
Abstract	This paper presents a new method to increase the accuracy in the estimates of the autoregressive (AR) model obtained by the Kumaresan-Tuft (KT) method proposed in 1982. In the KT method, there are the following two problems to be solved. (1) It is necessary to select the appropriate order of the AR-model before truncating the non-significant singular values obtained by the singular-value-decomposition (SVD). (2) There are errors in the selection of the signal- and noise-subspaces, which are determined by the noisy data matrix. Thus, the resultant singular values cannot be neglected even for the higher orders. Thus, truncation of the high order singular values by the predetermined order causes the bias error in the resultant AR parameter estimates. By introducing a tapering window into the truncation of high order non-significant singular values, the mean squared error of the estimates is certainly reduced. This paper also presents a new procedure to design an optimum tapering window for estimating AR model parameters without using any non-linear optimisation procedure.<>
File Size	281746
File Format	PDF
ISBN	0780317750
ISSN	15206149
DOI	10.1109/ICASSP.1994.389777
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-19
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Electronic mail Tiles Design optimization Additive noise Signal restoration
Content Type	Text
Resource Type	Article

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