Adaptive Kalman filter algorithm based on exponential attenuating factor for integrated navigation system

被引：0
作者：
Zeng Q. [1 ]
Zhao T. [1 ]
Zhao B. [1 ,2 ]
Liu J. [1 ]
Zhu X. [1 ]
机构：
[1] Navigation Research Center, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Jin Cheng College, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
| 1600年 / Editorial Department of Journal of Chinese Inertial Technology卷 / 29期
关键词：
Adaptive Kalman filter; Attenuating factor; Fault detection; Integrated navigation; Measurement noise;
D O I：
10.13695/j.cnki.12-1222/o3.2021.03.005
中图分类号：
学科分类号：
摘要：
In integrated navigation, the change of noise statistical characteristics caused by abnormal measurement information will decrease filtering accuracy or even lead to divergence. In order to solve the problem that the Sage-Husa adaptive algorithm has strong dependence on attenuating factor to estimate the system noise parameters, an adaptive filter algorithm for integrated navigation system based on the exponential attenuating factor is proposed. Based on the study of the statistical characteristics of measurement noise judged by fault detection function, a dynamic attenuating factor model based on exponential function is constructed to improve the navigation accuracy in abnormal measurement information. Compared with the Kalman filter, Sage-Husa adaptive Kalman filter, and sliding attenuating factor adaptive Kalman filter, the navigation performance of the proposed algorithm is significantly improved in the presence of measurement anomalies. The navigation position accuracy of simulation and vehicle experiments is enhanced by more than 20%. © 2021, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
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页码：307 / 313
页数：6
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