LSMI Algorithm Based on Inverse QR Decomposition

Author

Jian-shu, Cao ; Xue-gang, Wang

Author_Institution

Coll. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu

Volume

1

fYear

2006

fDate

38869

Firstpage

262

Lastpage

265

Abstract

Diagonal loaded sample-matrix inversion (LSMI) algorithm requires a high computational complexity. Thus, a new computationally efficient implementation for LSMI algorithm is presented, which is based on an inverse QR decomposition. The new method inserts the diagonal loading by only setting initial inverse Cholesky factor. It can offer good numerical properties and support parallel implementation with VLSI while avoiding backsubstitution operations. Hence, the new method can be applied for real-time signal processing. Simulations support the new algorithm

Keywords

VLSI; computational complexity; matrix decomposition; matrix inversion; signal processing; Cholesky factor; LSMI algorithm; VLSI; computational complexity; diagonal loaded sample-matrix inversion; inverse QR decomposition; parallel implementation; real-time signal processing; Array signal processing; Computational complexity; Covariance matrix; Educational institutions; Matrices; Matrix decomposition; Maximum likelihood estimation; Robustness; Signal processing algorithms; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems Proceedings, 2006 International Conference on

Conference_Location

Guilin

Print_ISBN

0-7803-9584-0

Electronic_ISBN

0-7803-9585-9

Type

conf

DOI

10.1109/ICCCAS.2006.284631

Filename

4063875