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In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding

Published online by Cambridge University Press:  21 June 2017

Haijian Xue ,
Xiaosong Guo ,
Zhaofa Zhou  and
Kunming Wang
Haijian Xue*
Affiliation:
(High-Tech Institute of Xi'an, Xi'an 710025, China)
Xiaosong Guo
Affiliation:
(High-Tech Institute of Xi'an, Xi'an 710025, China)
Zhaofa Zhou
Affiliation:
(High-Tech Institute of Xi'an, Xi'an 710025, China)
Kunming Wang
Affiliation:
(High-Tech Institute of Xi'an, Xi'an 710025, China)
*
(E-mail: [email protected])
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Abstract

In-motion alignment plays an important role in improving the manoeuvring capability of a vehicle, and allows the initialisation of a Strapdown Inertial Navigation System (SINS) while moving. Odometer (OD) aided in-motion alignment is widely adopted owing to its fully self-contained characteristic. This paper proposes a complete in-motion alignment algorithm for a vehicle-carried SINS based on odometer aiding, in which an in-motion coarse alignment method using the integration form of the velocity update equation in the body frame to give a rough initial angle is introduced and a new measurement equation in the body frame with a Kalman filter (KF) for the in-motion fine alignment is established. The advantages of the proposed method are verified by simulation and measured data.

Keywords

In-motion alignmentOdometer (OD)Attitude optimisationKalman filter (KF)
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1349 - 1366
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Ali, J. and Ushaq, M. (2009). A consistent and robust Kalman filter design in-motion of inertial navigation system. Measurement, 42, 577–582.Google Scholar
Bar-Itzhack, I. Y. (1996). REQUEST: a recursive QUEST algorithm for sequential attitude determination. Journal of Guidance, Control, and Dynamics, 19(5), 1034–1038.CrossRefGoogle Scholar
Chang, G. (2015). Fast two-position initial alignment for SINS using velocity plus angular rate measurements. Advance in Space Research, 56, 1331–1342.Google Scholar
Chang, L. B., Li, J. S. and Chen, S. Y. (2015). Initial alignment by attitude estimation for strapdown inertial navigation systems. IEEE Transactions on Instrumentation and Measurement, 64(3), 2015.Google Scholar
Dissanayake, G., Sukkarieh, S. and Nebot, E. (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), 731–747.Google Scholar
Fang, J. and Wan, D. (1996). A fast initial alignment method for strapdown inertial navigation system on stationary base. IEEE Transactions on Aerospace and Electronic Systems, 32(4), 1501–1504.Google Scholar
Fu, Q. W., Qin, Y. Y., Li, S.H. and Wang, H. M. (2012). Inertial navigation algorithm aided by motion constraints of vehicle. Journal of Chinese Inertial Technology, 20(6), 640–643.Google Scholar
Godha, S. and Cannon, M. E. (2007). GPS/MEMS INS integrated system for navigation in urban areas. GPS Solutions, 11(3), 193–203.Google Scholar
Gu, D., El-Sheimy, N., Hassan, T. and Syed, Z. (2008). Coarse alignment for marine SINS using gravity in the inertial frame as a reference. In Proceedings of Position, Location and Navigation Symposium, Monterey, CA.Google Scholar
Han, S. and Wang, J. (2010). A novel initial alignment scheme for low-cost INS aided by GPS for land vehicle applications. The Journal of Navigation, 63(4), 663–680.Google Scholar
Hong, W., Han, K., Lee, C. and Paik, B. (2010). Three stage in flight alignment with covariance shaping adaptive filter for the strapdown inertial navigation system (SDINS). AIAA Guidance, Navigation and Control Conference. Toronto, Ontario, Canada.Google Scholar
Jalving, B., Gade, K., Svartveit, K., Willumsen, A. and Sqrhagen, R. (2004). DVL velocity aiding in the HUGIN 1000 integrated inertial navigation system. Modeling, Identification and Control, 25(4), 223–235.CrossRefGoogle Scholar
Li, H., Pan, Q., Wang, X., Jiang, X. and Deng, L. (2015). Kalman filter design for initial precision alignment of a strapdown inertial navigation system on a rocking base. The Journal of Navigation, 68, 184–195.Google Scholar
Li, J., Xu, J., Chang, L. and Zha, F. (2014). An improved optimal method for initial alignment. The Journal of Navigation, 67, 727–736.Google Scholar
Pei, F. J., Zhu, L. and Zhao, J. (2015). Initial self-alignment for marine rotary SINS using novel adaptive Kalman filter. Mathematical Problems in Engineering, 2015, 1–12.Google Scholar
Shuster, M. D. and Oh, S. D. (1981). Three-axis attitude determination from vector observations. J. Guidance and Control, 4(1), 70–77.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 and Measurement, 60(6), 1930–1941.Google Scholar
Titterton, D. H. and Weston, J. L. (2004). Strapdown inertial navigation technology (2nd ed). London: The Institute of Electrical Engineers.Google Scholar
Veremeenko, K. K. and Savel'ev, V. M. (2013). In flight alignment of a strapdown inertial navigation system of an unmanned aerial vehicle. Journal of Computer and Systems Sciences International, 52(1), 106–116.Google Scholar
Wahba, G. (1965). A least squares estimate of spacecraft attitude. SIAM Review, 7(3), 409–411.Google Scholar
Wang, X. L. (2009). Fast alignment and calibration algorithms for inertial navigation system. Aerospace Science and Technology, 13, 204–209.Google Scholar
Wang, Y. G., Yang, J. S., Yu, Y. and Lei, Y. L. (2013). On-the-move alignment for SINS based on odometer aiding. Systems Engineering and Electronics, 35(5), 1060–1063.Google Scholar
Wu, M., Wu, Y., Hu, X. and Hu, D. (2011). Optimization-Based Alignment for Inertial Navigation Systems: Theory and algorithm. Aerospace Science and Technology, 15(1), 1–17.Google Scholar
Wu, Y. and Pan, X. (2013). Velocity/position integration formula part I: application to in-flight coarse alignment. IEEE Transactions on Aerospace and Electronic Systems, 49(2), 1006–1023.Google Scholar
Wu, Y., Zhang, H., Wu, M., Hu, X. and Hu, D. (2012). Observability of strapdown INS alignment: a global perspective. IEEE Transactions on Aerospace and Electronic Systems, 48(1), 78–102.Google Scholar
Xiong, J., Guo, H. and Yang, Z. H. (2014). A two-position SINS initial alignment method based on gyro information. Advance in Space Research, 53, 1657–1663.Google Scholar
Yan, G. M. and Qin, Y. Y. (2007). Novel approach to in-flight alignment of micro-mechanical SINS/GPS with heading uncertainty. Chinese Journal of Sensors and Actuators, 20(1), 238–242.Google Scholar
Yang, B., Peng, P. L., Wang, Y. G. and Zhou, X. G. (2013). Alignment method of strapdown inertial navigation system aided by odometer on moving base. Journal of Chinese Inertial Technology, 21(3), 298–307.Google Scholar
Zhang, Y. G., Huang, Y. L., Wu, Z. M. and Li, N. (2014). Moving state marine SINS initial alignment based on high degree CKF. Mathematical Problems in Engineering, 2014, 1–8.Google Scholar
Zhao, X. M., Zhao, S., Guo, Y. G., Wang, X. L., Zhou, L. F. and Wang, Q. (2015). In-motion alignment based on strong tracking filter. Journal of Chinese Inertial Technology, 23(2), 141–144.Google Scholar