Tian Guangliang
Inner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, ChinaZhang Lijie
Inner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, ChinaLi Zhiyu
Inner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, ChinaInner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, China
TP2;TN92
In view of the abnormal and short-term missing of UWB positioning information in UWB/INS integrated location, UWB/INS integrated location method based on improved robust kalman filter and SVR is proposed. In this method, the robust kalman filter (RKF) is improved. The improved IGG3 weight function is used to modify the innovation piecewise in order to reduce the influence of abnormal measurement information on the filtering result. When the UWB signal is normal, the position error is estimated with the improved RKF. When the UWB signal is missing, the position error is estimated with the online training SVR model, and the carrier position information is corrected according to the estimated or predicted position error. The experimental results show that the proposed method can not only reduce the integrated positioning error by 33.33% when the UWB signal is normal, but also online training SVR model can improve the performance of localization algorithm significantly compared with fixed SVR model and can continue to effectively locate when the UWB signal is short-term missing, reducing the integrated positioning error by 29.63%.
