A constrained least square and trimmed least square method for multisensor data fusion

Author

Shi, Haiyan ; Jing, Zhongliang ; Leung, Henry

Author_Institution

Dept. of Electr. & Comput. Eng., Shanghai Jiao Tong Univ., China

Volume

1

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

864

Abstract

Though neural data fusion algorithms based on a linearly constrained least square (LCLS) method solve the ill-conditioned and singular matrix problems that arise in the LCLS method, they don´t perform well when there are impulsive noises attached to several sensors. In this paper, a data fusion algorithm based on a constrained least squares (LS) and trimmed least squares (TLS) method is proposed. On one hand, it inherits the unbiased statistical property and the merit that no priori knowledge about the noise covariance is needed. On the other hand, it is more robust and has better results than LCLS and linearly constrained trimmed least squares (LCTLS).

Keywords

impulse noise; least squares approximations; matrix algebra; sensor fusion; constrained least square; impulsive noise; linearly constrained least squares method; multisensor data fusion algorithm; singular matrix problems; trimmed least squares method; unbiased statistical property; Aerospace control; Aerospace engineering; Covariance matrix; Gaussian noise; Kalman filters; Least squares methods; Noise measurement; Noise robustness; Sensor fusion; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on

Conference_Location

Nanjing

Print_ISBN

0-7803-7702-8

Type

conf

DOI

10.1109/ICNNSP.2003.1279413

Filename

1279413