Accurate estimation of AR model by tapered SVD without rank determination

Hiroshi Kanai, Noriyoshi Chubachi

Research output: Contribution to journalConference articlepeer-review

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. 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, whicli 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. In this paper, 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 optimization procedure.

Original languageEnglish
Article number389777
Pages (from-to)IV473-IV476
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
DOIs
Publication statusPublished - 1994 Jan 1
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: 1994 Apr 191994 Apr 22

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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