Title :
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
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;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279413
