@article{Du2012, 
author = {Xiongjie Du and Yue Wang and Xiuming Shan},
title = {Robust Sensor Bias Estimation for Ill-Conditioned Scenarios},
year = {2012},
journal = {Tsinghua Science and Technology},
volume = {17},
number = {3},
pages = {319-323},
keywords = {data fusion, sensor bias estimation, ill-conditioning},
url = {https://www.sciopen.com/article/10.1109/TST.2012.6216763},
doi = {10.1109/TST.2012.6216763},
abstract = {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 compared with the classical GLS method. The mean square error is given for both methods as a criterion to determine when to use this azimuth-GLS method. Furthermore, the separation boundary between the azimuth-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.}
}