Title :
Application of Robust Kalman Filtering to Integrated Navigation Based on Inertial Navigation System and Dead Reckoning
Author :
Dai, Hu ; Li, Jianxun ; Jin, Huiming
Author_Institution :
Dept. of Autom., Shanghai Jiaotong Univ., Shanghai, China
Abstract :
Aiming at solving the problem that the integrated navigation system which consists of inertial navigation system (INS) and dead reckoning (DR) has outliers and disturbance in measurement, as well as uncertainty in modeling, this paper proposes a new data processing method based on the technology of robust Kalman filtering (KF). The robust estimation is adopted to eliminate the outliers of the measurement of the sensors, thus the navigation information can be available for INS and DR respectively. Then for the INS/DR integrated navigation, the robust filtering is applied to resolve the modeling uncertainty. With this proposed method, experimental results show that the effect of the outliers in the measurement is eliminated, meanwhile, the robustness of the system is guaranteed, and therefore, the accuracy of integrated navigation is ensured.
Keywords :
Kalman filters; inertial navigation; INS-DR integrated navigation system; data processing method; dead reckoning; inertial navigation system; robust Kalman filtering; robust estimation; sensor measurement; Filtering; Global Positioning System; Mathematical model; Measurement uncertainty; Position measurement; Robustness; DR; INS; integrated navigation system; robust estimation; robust filtering;
Conference_Titel :
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-8432-4
DOI :
10.1109/AICI.2010.162