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A novel SINS/CNS Integrated Navigation Method Using Model Constraints for Ballistic Vehicle Applications

Published online by Cambridge University Press:  11 July 2017

Dingjie Wang*
Affiliation:
(Staff Room of Flight Dynamics and Control, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, Hunan, 410073, China)
Hanfeng Lv
Affiliation:
(Staff Room of Flight Dynamics and Control, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, Hunan, 410073, China)
Jie Wu
Affiliation:
(Staff Room of Flight Dynamics and Control, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, Hunan, 410073, China)
*

Abstract

The major challenge of current Strapdown Inertial Naviagtion System/Celestial Navigation System (SINS/CNS) integrated systems is navigation accuracy degradation due to the failure to accurately estimate the accelerometer bias even when using stellar refraction information (e.g. the number of refraction stars is less than three). To solve this problem, this paper presents a new method for improving the accuracy of the traditional inertial-based integrated systems installed on ballistic vehicles. In an analogy with nonholonomic constraints in land navigation, this algorithm exploits the constraints that govern the motion of a ballistic vehicle in the free flight phase to obtain accelerometer bias observations. Improvements in dynamic equations are used to reduce the propagation of navigation errors, and high-rate virtual constraints are used to reduce the impact of bias errors. An information filter is devised to fuse the multi-rate observations from multiple sources, i.e. SINS, CNS and model constraints. The proposed method is also evaluated by long-range ballistic vehicle navigation simulations. The results indicate that the proposed constrained algorithm can address the degradation problem with remarkable accuracy improvements without adding extra sensors, enhancing the SINS-based navigation performance for ballistic applications.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Ali, J. and Fang, J. (2006). SINS/ANS integration for augmented performance navigation solution using unscented Kalman filtering. Aerospace Science and Technology, 10(3), 233238.CrossRefGoogle Scholar
Ali, J. and Fang, J. (2009). Realization of an autonomous integrated suite of strapdown astro-inertial navigation systems using unscented particle filtering. Computers and Mathematics with Applications, 57(2), 169187.Google Scholar
Bar-Shalom, Y., Li, X. and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation-Theory Algorithms and Software. John Wiley & Sons, Inc. 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 and Automation, 17(5), 731747.CrossRefGoogle Scholar
Fang, J. and Ning, X. (2006). Principle and Application of Celestial Navigation System. Beihang Press.Google Scholar
Guan, X., Wang, X., Fang, J. and Feng, S. (2014). An innovative high accuracy autonomous navigation method for the Mars rovers. Acta Astronautica, 104, 266275.CrossRefGoogle Scholar
He, B., Li, H. and Zhang, B. (2013). Analysis of transfer orbit deviation propagation mechanism and robust design for manned lunar landing. Acta Physica Sinica, 62(19), 9198.Google Scholar
He, Z., Wang, X. and Fang, J. (2014). An innovative high-precision SINS/CNS deep integrated navigation scheme for the Mars rover. Aerospace Science and Technology, 39, 559566.CrossRefGoogle Scholar
Huang, W., Xie, H., Shen, C. and Li, J. (2016). A robust strong tracking cubature Kalman filter for spacecraft attitude estimation with quaternion constraint. Acta Astronautica, 121, 153163.Google Scholar
Ning, X., Gui, M., Xu, Y., Bai, X. and Fang, J. (2016). INS/VNS/CNS integrated navigation method for planetary rovers. Aerospace Science and Technology, 48, 102114.Google Scholar
Ning, X. and Liu, L. (2014). A Two-Mode INS/CNS Navigation Method for Lunar Rovers. IEEE Transactions on Instrumentation and Measurement, 63(9), 21702179.CrossRefGoogle Scholar
Ning, X., Wang, L., Bai, X. and Fang, J. (2013). Autonomous satellite navigation using starlight refraction angle measurements. Advances in space research, 51, 17611772.CrossRefGoogle Scholar
Niu, X., Li, Y., Zhang, Q., Cheng, Y. and Shi, C. (2012). Observability Analysis of Non-Holonomic Constraints for Land Vehicle Navigation Systems. Journal of Global Positioning Systems, 11(1), 8088.CrossRefGoogle Scholar
Qian, H., Sun, L., Cai, J. and Huang, W. (2014). A starlight refraction scheme with single star sensor used in autonomous satellite navigation system. Acta Astronautica, 96, 4552.CrossRefGoogle Scholar
Qian, H., Sun, L., Cai, J. and Peng, Y. (2013). A novel navigation method used in a ballistic missile. Measurement Science and Technology, 24, 13661374.Google Scholar
Quan, W., Li, J., Gong, X. and Fang, J. (2015). INS/CNS/GNSS Integrated Navigation Technology. Springer Publishing Company, Inc. Google Scholar
Titterton, D.H. and Weston, J.L. (2004). Strapdown Inertial Navigation Technology (2nd Edition). Peter Peregrinus, Ltd. Google Scholar
Wang, G., Ning, S., Jin, S. and Sun, C. (2004). Research on starlight atmospheric refraction model in autonomous satellite navigation. Journal of China University of Mining and Technology, 33(6), 616620.Google Scholar
Wang, R., Xiong, Z., Liu, J. and Shi, L. (2016). A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations. Proceedings of the Institution of Mechanical Engineers Part G-Journal of Aerospace Engineering, 230(4), 698712.Google Scholar
Wang, X., Zhang, Q. and Li, H. (2014). An autonomous navigation scheme based on starlight, geomagnetic and gyros with information fusion for small satellites. Acta Astronautica, 94, 708717.Google Scholar
Wen, Y., Zhang, Z., Wu, J. (2012). High-Precision Navigation Approach of High-orbit Spacecraft Based on Retransmission Communication Satellites. The Journal of Navigation, 65(2), 351362.Google Scholar
White, R.L. and Gounley, R.B. (1987). Satellite autonomous navigation with SHAD. The Charles Stark Draper Laboratory, Inc., 115116.Google Scholar
Xu, F. and Fang, J. (2014). Velocity and position compensation using strapdown inertial navigation system/celestial navigation system integration based on ensemble neural network. Aerospace Science and Technology, 12(4), 302307.CrossRefGoogle Scholar
Yang, S., Yang, G., Zhu, Z. and Li, J. (2016). Stellar Refraction-Based SINS/CNS Integrated Navigation System for Aerospace Vehicles. Journal of Aerospace Engineering, 29(2), 111.Google Scholar
Zarchan, P. (2012). Tactical and Strategic Missile Guidance (Sixth Edition). American Institute of Aeronautics and Astronautics, Inc. Google Scholar
Zhang, H., Zheng, W. and Tang, G. (2012). Stellar/inertial integrated guidance for responsive launch vehicles. Aerospace Science and Technology, 18(1), 3541.CrossRefGoogle Scholar
Zhang, L., Yang, H., Zhang, S., Cai, H. and Qian, S. (2014). Strapdown stellar-inertial guidance system for launch vehicle. Aerospace Science and Technology, 33(1), 122134.Google Scholar