A least-squares approach to joint Schur decomposition

Author

Abed-Meraim, K. ; Hua, Y.

Author_Institution

Dept. of Electr. Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

4

fYear

1998

fDate

12-15 May 1998

Firstpage

2541

Abstract

We address the problem of joint Schur decomposition (JSD) of several matrices. This problem is of great importance for many signal processing applications such as sonar, biomedicine, and mobile communications. We first present a least-squares (LS) approach for computing the JSD. The LS approach is shown to coincide with that proposed intuitively by Haardt et al. (1996), thus establishing the optimality of their criterion in the least-squares sense. Following the LS criterion, we then propose new Jacobi-like algorithms that extend and improve the existing JSD algorithms. An application of the new JSD algorithm to multidimensional harmonic retrieval is also presented

Keywords

harmonic analysis; least squares approximations; matrix decomposition; signal processing; JSD algorithms; Jacobi-like algorithms; biomedicine; joint Schur decomposition; least-squares approach; matrices; mobile communications; multidimensional harmonic retrieval; signal processing; sonar; Costs; Error analysis; Estimation error; Ink; Jacobian matrices; Matrix decomposition; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681669

Filename

681669