SLIDING WINDOWS AND LATTICE ALGORITHMS FOR COMPUTING QR FACTORS IN THE LEAST-SQUARES THEORY OF LINEAR PREDICTION

被引：6

作者：

DEMEURE, CJ

SCHARF, LL

机构：

[1] Electrical and Computer Engineering Department, University of Colorado, Boulder

来源：

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING | 1990年 / 38卷 / 04期

关键词：

D O I：

10.1109/29.52714

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this correspondence we pose a sequence of linear prediction problems that differ a little from those previously posed. The solutions to these problems introduce a family of “sliding” window techniques into the least squares theory of linear prediction. By using these techniques we are able to QR factor the Toeplitz data matrices that arise in linear prediction. The matrix Q is an orthogonal version of the data matrix and the matrix R is a Cholesky factor of the experimental correlation matrix. Our QR and Cholesky algorithms generate generalized reflection coefficients that may be used in the usual ways for analysis, synthesis, or classification. © 1990 IEEE

引用

页码：721 / 725

页数：5

共 18 条

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