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Velocity-aided In-motion Alignment for SINS Based on Pseudo-Earth Frame

Published online by Cambridge University Press:  10 August 2017

Meng Liu ,
Guangchun Li ,
Yanbin Gao ,
Shutong Li  and
Lianwu Guan
Meng Liu
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Guangchun Li*
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Yanbin Gao
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Shutong Li
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
Lianwu Guan
Affiliation:
(College of Automation, Harbin Engineering University, Harbin 150001, China)
*
(E-mail: [email protected])
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Abstract

Approaching the problem from the internal factors and in particular the inherent state model of a Kalman Filter, this paper presents a novel Strapdown Inertial Navigation System (SINS) modelling, which is obtained with a pseudo-north-oriented mechanisation in a pseudo-geographic frame. Improved modelling associated with the backward algorithm is proposed to achieve velocity-aided in-motion alignment. Compared with traditional algorithms, the proposed method can eliminate the influence of alignment model on the performance of initial alignment caused by SINS modelling. On the other hand, the backward process can still be used to accelerate the process of alignment. As a result, the proposed method is expected to assist those methods only considered from external factors (such as coarse accuracy, process noise, measurement noise, and so on) to improve the stability and robustness of a velocity-aided in-motion alignment system and to solve the modelling problem of high latitude alignment without sacrificing alignment accuracy. Finally, simulations and field experiments with a navigation-grade SINS demonstrate the superior performance of the proposed method.

Keywords

In-motion alignmentPseudo-Earth frameHigh latitude alignmentKalman Filter (KF)
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 221 - 240
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Alahyari, A., Rozbahani, S.G. and Habibzadeh, A. (2011). INS/DVL Positioning System using Kalman Filter. Australian Journal of Basic & Applied Sciences, 5(9), 1123–1129.Google Scholar
Ali, J. And Ushaq, M. (2009). A consistent and robust Kalman filter design for in-motion alignment of inertial navigation system. Measurement, 42(4), 577–582.Google Scholar
Chang, L., Hu, B. and Li, Y. (2015a). Backtracking Integration for Fast Attitude Determination-Based Initial Alignment. IEEE Transactions on Instrumentation & Measurement, 64(3), 795–803.CrossRefGoogle Scholar
Chang, L., Li, J. and Chen, S. (2015b). Initial Alignment by Attitude Estimation for Strapdown Inertial Navigation Systems. IEEE Transactions on Instrumentation & Measurement, 64(3), 784–794.CrossRefGoogle Scholar
Gao, S., Wei, W., Zhong, Y. and Feng, Z. (2014a). Rapid alignment method based on local observability analysis for strapdown inertial navigation system. Acta Astronautica, 94(2), 790–798.CrossRefGoogle Scholar
Gao, S., Xue, L., Zhong, Y. and Gu, C. (2015). Random weighting method for estimation of error characteristics in SINS/GPS/SAR integrated navigation system. Aerospace Science & Technology, 46, 22–29.CrossRefGoogle Scholar
Gao, W., Ben, Y., Zhang, X. and Li, Q. and Yu, F. (2011). Rapid Fine Strapdown INS Alignment Method under Marine Mooring Condition. IEEE Transactions on Aerospace & Electronic Systems, 47(4), 2887–2896.CrossRefGoogle Scholar
Gao, W., Li, J. and Zhou, G. (2014b). Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems. Journal of Navigation, 68(1), 142–161.CrossRefGoogle Scholar
Hong, H.S., Lee, J.G. and Park, C.G. (2004). Performance improvement of in-flight alignment for autonomous vehicle under large initial heading error.IEE Proceedings – Radar Sonar and Navigation, 151(1), 57–62.Google Scholar
Hu, J. and Cheng, X. (2014). A new in-motion initial alignment for land-vehicle SINS/OD integrated system. Proceedings of Position, Location and Navigation Symposium-PLANS 2014, Monterey, CA.CrossRefGoogle Scholar
Kang, T., Fang, J. and Wang, W. (2012). Quaternion-Optimization-Based In-Flight Alignment Approach for Airborne POS. IEEE Transactions on Instrumentation & Measurement, 61(11), 2916–2923.Google Scholar
Li, J., Xu, J., Chang, and L. Zhan, F. (2014). An Improved Optimal Method For Initial Alignment. Journal of Navigation, 67(4), 727–736.Google Scholar
Li, Q., Ben, Y., Yu, F. and Tan, J. (2015). Transversal Strapdown INS based on Reference Ellipsoid for Vehicle in Polar Region. IEEE Transactions on Vehicular Technology, PP(99), 1–1.Google Scholar
Li, W., Wang, J., Lu, L. and Wu, W. (2013a). A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques. Sensors, 13(1), 1046–63.Google Scholar
Li, W., Wu, W., Wang, J. and Lu, L. (2013b). A Fast SINS Initial Alignment Scheme for Underwater Vehicle Applications. Journal of Navigation, 66(2), 181–198.Google Scholar
Lu, J., Xie, L and Li, B. (2016). Analytic Coarse Transfer Alignment Based on Inertial Measurement Vector Matching and Real-Time Precision Evaluation. IEEE Transactions on Instrumentation & Measurement, 65(2), 355–364.CrossRefGoogle Scholar
Liu, M., Gao, Y., Li, G., Guang, X. and Li, S. (2016). An Improved Alignment Method for the Strapdown Inertial Navigation System (SINS). Sensors,16(5).Google ScholarPubMed
Marantos, P., Koveos, Y. and Kyriakopoulos, K.J. (2015). UAV State Estimation Using Adaptive Complementary Filters, IEEE Transactions on Control Systems Technology, PP(99), 1–13.Google Scholar
Pei, F., Zhu, L. and Zhao, J. (2015). Initial Self-Alignment for Marine Rotary SINS Using Novel Adaptive Kalman Filter. Mathematical Problems in Engineering, 2015(31), 1–12.Google Scholar
Salychev, O.S. (2004). Applied Inertial Navigation: problems and solutions. Russia: BMSTU Press.Google Scholar
Schimelevich, L. and Naor, R. (1996). New approach to coarse alignment. Proceedings of IEEE PLANS, Atlanta, GA.CrossRefGoogle Scholar
Shin, E.H. (2005). Estimation techniques for low-cost inertial navigation. University of Calgary, Calgary, AB.Google Scholar
Silson, P.M.G. (2011). Coarse Alignment of a Ship's Strapdown Inertial Attitude Reference System Using Velocity Loci. IEEE Transactions on Instrumentation & Measurement, 60(6), 1930–194.Google Scholar
Sun, F., Wang, Q., Zhao, Q. and Wang, C. (2013). Research on the estimation method of DVL velocity error based on double program in Fiber Optic Gyro SINS. Optik – International Journal for Light and Electron Optics, 124(24), 5344–5349.Google Scholar
Weng, T.P. (2008). An Adaptive Sensor Fusion Method with Applications in Integrated Navigation. Journal of Navigation, 61(61), 705–721.Google Scholar
Wu, Y. and Pan, X. (2013). Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment. IEEE Transactions on Aerospace & Electronic Systems, 49(2), 1006–1023.Google Scholar
Xiong, J., Guo, H. and Yang, Z.H. (2014). A Two-Position SINS Initial Alignment Method Based on Gyro Information. Advances in Space Research, 53(11), 1657–1663.Google Scholar
Yan, G., Yan, W. and Xu, D. (2008). On reverse navigation algorithm and its application to SINS gyro-compass in-movement alignment. Proceedings of IEEE ChineseControl Conference, Kunming, China.Google Scholar
Zhao, C., Qin, Y. and Yan, G. (2008). On-the-move alignment for strap-down inertial navigation system. Proceedingsof IEEEInformation and Automation, ICIA.CrossRefGoogle Scholar