Robust Sensor Bias Estimation for Ill-Conditioned Scenarios  

作　　者：Xiongjie Du Yue Wang Xiuming Shan 

机构地区：[1]Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Tsinghua Science and Technology》2012年第3期319-323,共5页清华大学学报（自然科学版（英文版）

摘　　要：Sensor bias estimation is an inherent problem in multi-sensor data fusion systems. Classical methods such as the Generalized Least Squares （GLS） method can have numerical problems with ill-conditioned sets which are common in practical applications. This paper describes an azimuth-GLS method that provides a solution to the ill-conditioning problem while maintaining reasonable accuracy com- pared with the classical GLS method. The mean square error is given for both methods as a criterion to de- termine when to use this azimuth-GLS method. Furthermore, the separation boundary between the azi- muth-GLS favorable region and that of the GLS method is explicitly plotted. Extensive simulations show that the azimuth-GLS approach is preferable in most scenarios.Sensor bias estimation is an inherent problem in multi-sensor data fusion systems. Classical methods such as the Generalized Least Squares （GLS） method can have numerical problems with ill-conditioned sets which are common in practical applications. This paper describes an azimuth-GLS method that provides a solution to the ill-conditioning problem while maintaining reasonable accuracy com- pared with the classical GLS method. The mean square error is given for both methods as a criterion to de- termine when to use this azimuth-GLS method. Furthermore, the separation boundary between the azi- muth-GLS favorable region and that of the GLS method is explicitly plotted. Extensive simulations show that the azimuth-GLS approach is preferable in most scenarios.

关 键 词：data fusion sensor bias estimation ILL-CONDITIONING 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] O174.55[自动化与计算机技术—控制科学与工程]