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

被引：0

作者：

Shi, HY ^{[1
]}

Jing, ZL ^{[1
]}

Leung, H ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Inst Aerosp Informat & Control, Shanghai 200030, Peoples R China

来源：

PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Though neural data fusion algorithms based on a linearly constrained least square (LCLS) method solve the ill-conditioned and singular matrix problems arisen in the LCLS method, they don't perform well when there are impulsive noises attached to several sensors. In this paper, data fusion algorithm based on a constrained least square (LS) and trimmed least square (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 square (LCTLS).

引用

页码：864 / 867

页数：4

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