Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter-Reference-Cited by-同舟云学术

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Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter

Published:2018-02 Issue:1 Volume:16 Page:129-140
ISSN:1598-6446
Container-title:International Journal of Control, Automation and Systems
language:en
Short-container-title:Int. J. Control Autom. Syst.

Author:

Gao Bingbing,Hu Gaoge,Gao Shesheng,Zhong Yongmin,Gu Chengfan

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Control and Systems Engineering

Link

http://link.springer.com/article/10.1007/s12555-016-0801-4/fulltext.html

Reference33 articles.

1. Y. Meng, S. S. Gao, Y. M. Zhong, G. G. Hu, and A. Subic, “Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration,” Acta Astronautica, vol. 120, pp. 171–181, April 2016. [click]

2. G. G. Hu, S. S. Gao, and Y. M. Zhong, “A derivative UKF for tightly coupled INS/GPS integrated navigation,” ISA transactions, vol. 56, pp. 135–144, May 2015. [click]

3. A. Noureldin, T. B. Karamat, M. D. Eberts, and A. El-Shafie, “Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications,” IEEE Transactions on Vehicular Technology, vol. 58, no. 3, pp. 1077–1096, March 2009. [click]

4. W. H. Wei, S. S. Gao, Y. M. Zhong, C. F. Gu, and A. Subic, “Random weighting estimation for systematic error of observation model in dynamic vehicle navigation,” International Journal of Control, Automation and Systems, vol. 14, no. 2, pp. 514–523, April 2016. [click]

5. Y. Zhao, S. S. Gao, J. Zhang, and Q. N. Sun, “Robust predictive augmented unscented Kalman filter,” International Journal of Control, Automation and Systems, vol. 12, no. 5, pp. 996–1004, October 2014. [click]

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