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Ocean Vehicle Inertial Navigation Method based on Dynamic Constraints

Published online by Cambridge University Press:  16 May 2018

Jiazhen Lu  and
Lili Xie
Jiazhen Lu*
Affiliation:
(Beihang University, 100191 Beijing, People's Republic of China)
Lili Xie
Affiliation:
(Beihang University, 100191 Beijing, People's Republic of China)
*
(E-mail: ljzbuaa@163.com)
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Abstract

This paper proposes a dynamic aided inertial navigation method to improve the attitude accuracy for ocean vehicles. The proposed method includes a dynamic identification algorithm and the utilisation of dynamic constraints to derive additional observations. The derived additional observations are used to update the filters and limit the attitude error based on the dynamic knowledge. In this paper, two dynamic conditions, constant speed cruise and quasi-static, are identified and corresponding additional velocity and position observations are derived. Simulation and experimental results show that the proposed method can improve and guarantee the accuracy of the attitude. The method can be used as a backup method to bridge external information outages or unavailability. Both the features of independence of external support and integrity of the Inertial Navigation System (INS) are enhanced.

Keywords

Ocean vehicleDynamic constraintInertial navigation systemAttitudeKalman filter
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 6 , November 2018 , pp. 1553 - 1566
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Atia, M. M., Noureldin, A. and Korenberg, M. (2011). Gaussian process regression approach for bridging GPS outages in integrated navigation systems. Electronics Letters, 47, 52–53.Google Scholar
Chen, L. and Fang, J. (2014). A hybrid prediction method for bridging GPS outages in high-precision POS application. IEEE Transactions on Instrumentation & Measurement, 63, 1656–1665.Google Scholar
Chen, X., Shen, C., Zhang, W. B., Tomizuka, M., Xu, Y. and Chiu, K. (2013). Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages. Measurement, 46, 3847–3854.Google Scholar
Dissanayake, G., Sukkarieh, S., Nebot, E. and Durrant-Whyte, H. (2001). The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications. IEEE Transactions on Robotics & Automation, 17, 731–747.Google Scholar
Gao, W., Li, J., Zhou, G. and Li, Q. (2014). Adaptive Kalman filtering with recursive noise estimator for integrated sins/dvl systems. Journal of Navigation, 68, 142–161. http://www.docin.com/p-217252818.htmlGoogle Scholar
Hwang, D. H., Sang, H. O., Sang, J. L., Park, C. and Rizos, C. (2005). Design of a low-cost attitude determination GPS/INS integrated navigation system. GPS Solutions, 9, 294–311Google Scholar
iXblue. (2008). OCTANS User Guide II Part 2: OCTANS Surface User Guide, iXblue. II-37.Google Scholar
Noureldin, A., El-Shafie, A. and Bayoumi, M. (2011). GPS/INS integration utilizing dynamic neural networks for vehicular navigation. Information Fusion, 12, 48–57.Google Scholar
Wang, J. H. and Gao, Y. (2010). Land vehicle dynamics-aided inertial navigation. IEEE Transactions on Aerospace & Electronic Systems, 46, 1638–1653.Google Scholar
Wang, J. H., Gao, Y. and Zhang, A. Y. (2005). An Intelligent MEMS IMU-Based Land Vehicle Navigation System Enhanced by Dynamics Knowledge. Proceeding US ION 61st Ann Meeting. MA, USA, June 27–29.Google Scholar
Wang, Q., Diao, M., Gao, W., Zhu, M. and Xiao, S. (2015). Integrated navigation method of a marine strapdown inertial navigation system using a star sensor. Measurement Science & Technology, 26, 250–252.Google Scholar
Xu, B., Liu, Y., Shan, W., Zhang, Y. and Wang, G. (2014). Error analysis and compensation of gyrocompass alignment for sins on moving base. Mathematical Problems in Engineering, 2, 1–18.Google Scholar