A decomposition method for positive semidefinite matrices and its application to recursive parameter estimation

被引：1

作者：

Cao, LY ^{[1
]}

Schwartz, HM ^{[1
]}

机构：

[1] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada

来源：

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS | 2001年 / 22卷 / 04期

关键词：

positive semidefinite matrices; matrix decomposition; rank additivity; least squares method; recursive estimation;

D O I：

10.1137/S0895479899364027

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A matrix decomposition method for positive semidefinite matrices based on a given subspace is proposed in this paper. It is shown that any positive semidefinite matrix can be decomposed uniquely into two positive semidefinite parts with specified rank one of which is orthogonal to the subspace. This method is then compared with the rank-additivity decomposition, and the difference as well as the close connection between these two decompositions are given. Finally, the proposed decomposition method is used to develop a new recursive parameter estimation algorithm for linear systems.

引用

页码：1095 / 1111

页数：17

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