High-precision tightly integrated MIMU/GPS navigation algorithm based on adaptive attitude estimation

被引：0
|
作者：
Shan B. [1 ]
Zhang F. [1 ]
Yang B. [1 ]
Wang Y. [1 ]
Xue L. [1 ]
Teng H. [1 ]
机构：
[1] Department of Missile Engineering, Rocket Force University of Engineering, Xi'an
来源：
Zhang, Fujian (18811349731@163.com) | 2018年 / Editorial Department of Journal of Chinese Inertial Technology卷 / 26期
关键词：
Attitude estimation; Fuzzy logic; Integrated navigation; Tightly-coupled;
D O I：
10.13695/j.cnki.12-1222/o3.2018.06.010
中图分类号：
学科分类号：
摘要：
When the number of visible satellites is less than 4, the loosely coupled MIMU/GPS integrated navigation system can not work normally, and the tightly coupled system's navigation errors are susceptible to large fluctuations and even divergence in certain situations. To solve these problems, the influences of different observation information on the performance of MIMU/GPS integrated navigation system are compared and analyzed, and an adaptive attitude estimation method based on multi-model is proposed for the situation that the vehicle is turning frequently. A data adaptive fusion scheme is utilized to improve the performance of MIMU/GPS integrated navigation system through the fuzzy logic inference system with the input of vehicle maneuvers, which is an intelligent decision-making to yield the corresponding weight coefficient between the adaptive attitude estimation based on multi model and the attitude estimation based on filtering algorithm. The simulation results show that the speed precision of the proposed method is less than 0.1 m/s, and the position precision is less than 5 m, which are not affected by large angle steering of the vehicle and can continuously output high-precision navigation information. © 2018, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
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页码：760 / 767
页数：7
相关论文
共 18 条
  • [11] Nourmohammadi H., Keighobadi J., Decentralized INS/GNSS system with MEMS-grade inertial sensors using QR-factorized CKF, IEEE Sensors Journal, 17, 11, pp. 3278-3287, (2017)
  • [12] Lin M., Zheng Y., Liu T., Et al., Coupled integration of CSAC, MIMU, and GNSS for improved PNT performance, Sensors, 16, 5, (2016)
  • [13] Shen X.W., Chang R.H., Yuan D., GPS/MIMU integrated attitude estimation based on simple spherical simplex SRUKF, Journal of Chinese Inertial Technology, 25, 2, pp. 197-202, (2017)
  • [14] Kaneda Y., Adachi S., Kalman filter: from theory to applications, Journal of The Society of Instrument and Control Engineers, 56, 9, (2017)
  • [15] Liu G., Guo M.F., Zhang R., Et al., MIMU precision's influence on GNSS/MINS integrated navigation system performance by simulation analysis, Journal of Chinese Inertial Technology, 6, (2013)
  • [16] Hamel T., Mahony R., Attitude estimation on SO[3]based on direct inertial measurements, IEEE International Conference on Robotics and Automation, pp. 2170-2175, (2006)
  • [17] Tseng S.P., Li W.L., Sheng C.Y., Et al., Motion and attitude estimation using inertial measurements with complementary filter, 8th Asian Control Conference, pp. 863-868, (2011)
  • [18] Liu W.Y., Hao L.L., Huang S.J., Et al., A new study of neural network aided GPS/MEMS-INS integrated navigation, Journal of Geomatics Science and Technology, 4, pp. 336-341, (2014)
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