A Robust and Adaptive Unscented Kalman Filter for the Air Fine Alignment of the Strapdown Inertial Navigation System/GPS
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32870
A Robust and Adaptive Unscented Kalman Filter for the Air Fine Alignment of the Strapdown Inertial Navigation System/GPS

Authors: Jian Shi, Baoguo Yu, Haonan Jia, Meng Liu, Ping Huang


Adapting to the flexibility of war, a large number of guided weapons launch from aircraft. Therefore, the inertial navigation system loaded in the weapon needs to undergo an alignment process in the air. This article proposes the following methods to the problem of inaccurate modeling of the system under large misalignment angles, the accuracy reduction of filtering caused by outliers, and the noise changes in GPS signals: first, considering the large misalignment errors of Strapdown Inertial Navigation System (SINS)/GPS, a more accurate model is made rather than to make a small-angle approximation, and the Unscented Kalman Filter (UKF) algorithms are used to estimate the state; then, taking into account the impact of GPS noise changes on the fine alignment algorithm, the innovation adaptive filtering algorithm is introduced to estimate the GPS’s noise in real-time; at the same time, in order to improve the anti-interference ability of the air fine alignment algorithm, a robust filtering algorithm based on outlier detection is combined with the air fine alignment algorithm to improve the robustness of the algorithm. The algorithm can improve the alignment accuracy and robustness under interference conditions, which is verified by simulation.

Keywords: Air alignment, fine alignment, inertial navigation system, integrated navigation system, UKF.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 493


[1] Kong XY, Nebot E M, Durrant-Whyte H. Development of a nonlinear psi-angle model for large misalignment errors and its application in INS alignment and calibration (C). IEEE International Conference on Robotics & Automation, 1999:1430-1435.
[2] Kong XY. INS algorithm using quaternion model for low cost IMU (J). Robotics and Autonomous Systems, 2004, 46(4):221-246.
[3] Wei Chunling, Zhang Shuyue, Hao Shuguang. SINS Nonlinear Alignment with Large Azimuth Misalignment Angles (J). Aerospace Control, 2003, 21(4):25-35.
[4] Scherzinger B M. Inertial navigator error models for large heading uncertainty (C). Position Location & Navigation Symposium, 2002:477-484.
[5] Gul F, Fang JC, Gaho A A. GPS/SINS navigation data fusion using quaternion model and unscented Kalman filter (C). IEEE International Conference on Mechatronics & Automation, 2006:1854-1859.
[6] Bai M, Zhao XG, Hou ZG, et al. Application of an adaptive extended Kalman filter in SINS/GPS integrated navigation system (C). World Congress on Intelligent Control & Automation, 2008:2707-2712.
[7] Qin Yongyuan, Zhang Shuyue, Wang Chunhua, Theory of Kalman Filter and Integrated Navigation (M). Northwestern Polytechnical University Press, 2012.
[8] Zhong MY, Guo J, Zhou DH. Adaptive in-flight alignment of INS/GPS systems for aerial mapping (J). IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3):1184-1196.
[9] Zhu Bing, Xu Jiangning, Wu Miao, Robust adaptive UKF approach for underwater moving base initial alignment (J). Chinese Journal of Scientific Instrument, 2018,39(2):73-80.
[10] Cheng Jiao-jiao, Xiong Zhi, Yu Feng, Wu Xuan, Zhao Hui, Research on Algorithm of Robust Filtering in SINS/GPS/CNS Integrated Navigation System (J). Aeronautical Computing Technique, 2013(6):30-34.
[11] Wang DJ, Dong Y, Li QS, et al. Estimation of small UAV position and attitude with reliable in-flight initial alignment for MEMS inertial sensors (J). Metrology and Measurement Systems,2018,25(3):603-616.