Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T17:30:22.922Z Has data issue: false hasContentIssue false
  • English
  • Français

Autonomous In-motion Alignment for Land Vehicle Strapdown Inertial Navigation System without the Aid of External Sensors

Published online by Cambridge University Press:  29 June 2018

Qiangwen Fu Open the ORCID record for Qiangwen Fu [Opens in a new window] ,
Yang Liu Open the ORCID record for Yang Liu [Opens in a new window] ,
Zhenbo Liu ,
Sihai Li  and
Bofan Guan
Qiangwen Fu*
Affiliation:
(School of Automation, Northwestern Polytechnical University, China)
Yang Liu
Affiliation:
(School of Automation, Northwestern Polytechnical University, China)
Zhenbo Liu
Affiliation:
(School of Automation, Northwestern Polytechnical University, China)
Sihai Li
Affiliation:
(School of Automation, Northwestern Polytechnical University, China)
Bofan Guan
Affiliation:
(School of Automation, Northwestern Polytechnical University, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes a fully autonomous real-time in-motion alignment algorithm for Strapdown Inertial Navigation Systems (SINS) in land vehicle applications. Once the initial position is available, the vehicle can start a mission immediately with accurate attitude, position and velocity information determined within ten minutes. This is achieved by two tightly coupled stages, that is, real-time Double-vector Attitude Determination Coarse Alignment (DADCA) and Backtracking Fine Alignment (BFA). In the DADCA process, the vehicle motion is omitted to roughly estimate the attitude at the very start of the alignment. Meanwhile, attitude quaternions and velocity increments are extracted and recorded. The BFA process utilises the stored data and exploits the Non-Holonomic Constraints (NHC) of a vehicle to obtain virtual velocity measurements. A linear SINS/NHC Kalman filter with mounting angles as extended states is constructed to improve the fine alignment accuracy. The method is verified by three vehicle tests, which shows that the accuracy of alignment azimuth is 0·0358° (Root Mean Square, RMS) and the positioning accuracy is about 15 m (RMS) at the end of the alignment.

Keywords

In-motion alignmentBacktracking navigationNon-Holonomic Constraints (NHC)Land navigation
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 6 , November 2018 , pp. 1312 - 1328
Copyright
Copyright © The Royal Institute of Navigation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bimal Raj, K. and Joshi, A. (2015). In-motion alignment of inertial navigation system with doppler speed measurements. AIAA Guidance, Navigation, and Control Conference, Kissimmee, FL.Google Scholar
Chang, L., He, H. and Qin, F. (2016). In-Motion Initial Alignment for Odometer-Aided Strapdown Inertial Navigation System Based on Attitude Estimation. IEEE Sensors Journal, 17(3), 766–773.Google Scholar
Chang, L., Li, Y. and Xue, B. (2017). Initial Alignment for a Doppler Velocity Log-Aided Strapdown Inertial Navigation System With Limited Information. IEEE/ASME Transactions on Mechatronics, 22(1), 329–338.Google Scholar
Dissanayake, G., Sukkarieh, S., Nebot, E. and Whyte, H. D. (1999). A new algorithm for the alignment of inertial measurement units without external observation for land vehicle applications. IEEE International Conference on Robotics and Automation, 1999. Proceedings. IEEE, 1999, Detroit, MI.Google Scholar
Dissanayake, G., Sukkarieh, S., Nebot, E. and Whyte, H. D. (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
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
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. Position, Location and Navigation Symposium, 2008 IEEE/ION, Monterey, CA.Google Scholar
Hu, J. and Cheng., X. (2014). A new in-motion initial alignment for land-vehicle SINS/OD integrated system. IEEE/ION Position, Location and Navigation Symposium - PLANS 2014, Monterey, CA.Google Scholar
James, R.W. (2012). Spacecraft attitude determination and control. Springer Science & Business Media.Google Scholar
Jiang, Y. F., Xie, B. and Weng, J. (2013). SINS in-motion alignment and position determination for land-vehicle based on quaternion Kalman filter. Control Conference (CCC), 2013 32nd Chinese, Xi'an, China.Google Scholar
Jiang, Y. F. (1998). Error analysis of analytic coarse alignment methods. IEEE Transactions on Aerospace and Electronic Systems, 34(1), 334–337.Google Scholar
Kaygısız, B. H. and Şen, B. (2015). In-motion alignment of a low-cost GPS/INS under large heading error. The Journal of Navigation, 68(2), 355–366.Google Scholar
Kubo, Y., Fujioka, S., Nishiyama, M. and Sugimoto, S. (2006). Nonlinear filtering methods for the INS/GPS in-motion alignment and navigation. International Journal of Innovative Computing, Information and Control, 2(5), 1137–1151.Google Scholar
Li, W., Tang, K., Lu, L. and Wu, Y. (2013a). Optimization-based INS in-motion alignment approach for underwater vehicles. Optik - International Journal for Light and Electron Optics, 124(20), 4581–4585.Google Scholar
Li, W., Wang, J., Lu, L. and Wu, W. (2013b). A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques. Sensors, 13(1), 1046.Google Scholar
Li, W., Wu, W. and Lu, J. W. L. (2013c). A Fast SINS Initial Alignment Scheme for Underwater Vehicle Applications. Journal of Navigation, 66(2), 181–198.Google Scholar
Li, W., Wu, W., Wang, J. and Wu, M. (2014). A novel backtracking navigation scheme for Autonomous Underwater Vehicles. Measurement, 47(47), 496–504.Google Scholar
Liu, T., Xu, Q. and Li, Y. (2014). Adaptive filtering design for in-motion alignment of INS. 26th Chinese Control and Decision Conference, CCDC, Changsha., China.Google Scholar
Liu, Y., Li, S., Fu, Q., and Liu, Z. (2018). Impact Assessment of GNSS Spoofing Attacks on INS/GNSS Integrated Navigation System. Sensors, 18(5), Article 1433.Google Scholar
Pan, X. and Wu, Y. (2016). Underwater Doppler Navigation with Self-calibration. Journal of Navigation, 69(2), 295–312.Google Scholar
Peng, K. Y., Lin, C. A. and Chiang, K. W. (2013). Performance analysis of an AKF based tightly-coupled INS/GNSS integrated scheme with NHC for land vehicular applications. Transactions- Canadian Society for Mechanical Engineering, 37(3):503–513.Google Scholar
Qin, Y. Y. (2014). Inertial Navigation, 2nd Edition. China Science Press.Google Scholar
Rothman, Y., Klein, I. and Filin, S. (2015). Analytical Observability Analysis of INS with Vehicle Constraints. Navigation, 61(3):227–236.Google Scholar
Savage, P. G., (2000). Strapdown Analytics, 2nd. Strapdown Associates, Inc.Google Scholar
Shin, E. H. and El-Sheimy, N. (2004). An unscented Kalman filter for in-motion alignment of low-cost IMUs. IEEE/ION Position, Location and Navigation Symposium - PLANS 2004, Monterey, CA.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), 1930–1941.Google Scholar
Simon, D. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons.Google Scholar
Titterton, D. H. and Weston, J. L. (2004). Strapdown inertial navigation technology, 2nd. Institution of Electrical Engineers.Google Scholar
Wang, Q, Fu, M., Xiao, X. and Deng, Z. (2012). Automatic calibration and in-motion alignment of an odometer-aided INS. Proceedings of the 31st Chinese Control Conference, CCC, Hefei, China.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. (2013a). 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. and Pan, X. (2013b). Velocity/position integration formula part II: application to strapdown inertial navigation computation. IEEE Transactions on Aerospace and Electronic Systems, 49(2), 1024–1034.Google Scholar
Wu, Y., Wu, M., Hu, X. and Hu, D. (2009). Self-calibration for land navigation using inertial sensors and odometer: Observability analysis. AIAA Guidance, Navigation, and Control Conference and Exhibit, 2009, Chicago, IL.Google Scholar
Wu, Y. (2014). Versatile land navigation using inertial sensors and odometry: Self-calibration, in-motion alignment and positioning. Inertial Sensors and Systems Symposium (ISS), 2014 DGON, Karlsruhe, Germany.Google Scholar
Xu, J., He, H., Qin, F. and Chang, L. (2017). A Novel Autonomous Initial Alignment Method for Strapdown Inertial Navigation System. IEEE Transactions on Instrumentation and Measurement, 66(9), 2274–2282.Google Scholar