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In-motion Alignment for Low-cost SINS/GPS under Random Misalignment Angles

Published online by Cambridge University Press:  22 June 2017

Xiao Cui ,
Chunbo Mei ,
Yongyuan Qin ,
Gongmin Yan  and
Qiangwen Fu
Xiao Cui*
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
Chunbo Mei
Affiliation:
(No.203 Research Institute of China Ordnance Industries, China)
Yongyuan Qin
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
Gongmin Yan
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
Qiangwen Fu
Affiliation:
(College of Automation, Northwestern Polytechnical University, China)
*
(E-mail: [email protected])
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Abstract

This paper presents a reliable in-motion alignment algorithm for a low cost Strapdown Inertial Navigation System/Global Positioning System (SINS/GPS) combination under random misalignment angles, which transforms attitude alignment into an attitude estimation problem. Based on Rodrigues parameters, an alignment model with a linear state-space equation and a second order nonlinear measurement equation is established. Furthermore, by employing a Taylor expansion on the nonlinear measurement equation, we implement a second order Extended Kalman Filter (EKF2). The proposed method uses a single filter that can not only determine the initial attitude, but also estimate the sensor errors. In addition, a scheme is given for avoiding singularity, which makes the algorithm more widely suitable for random misalignment angles. Experimental ground tests are performed with a low-cost Micro-Electromechanical System (MEMS) SINS, which validates the efficacy of the proposed method. The performance compared to the traditional alignment algorithm is also given.

Keywords

In-motion alignmentInertial navigation systemRodrigues parametersSecond order Extended Kalman Filter (EKF2)
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1224 - 1240
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Bimal, R.K. and Ashok, J. (2015). In-motion Alignment of Inertial Navigation System with Doppler Speed Measurements. AIAA Guidance, Navigation, and Control Conference, Kissimmee, Florida.Google Scholar
Burak, H.K. and Bekir, S. (2015). In-motion Alignment of a Low-cost GPS/INS under Large Heading Error. The Journal of Navigation, 68, 355–366.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, 784–794.Google Scholar
Choukronun, D., Bar-Itzhack, I.Y. and Oshman, Y. (2004). Optimal-REQUEST algorithm for attitude determination. Journal of Guidance, Control, and Dynamic, 27, 418–425.Google Scholar
Daniele, M., Markley, F.L. and Puneer, S. (2007). Optimal Linear Attitude Estimator. Journal of Guidance, Control, and Dynamic, 30, 1619–1627.Google Scholar
Dissanayake, G., Sukkarieh, S., Nebot, E.M. 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, 731–747.CrossRefGoogle Scholar
Gu, G., EI-Sheimy, N. and Hassan, T. (2008). Coarse alignment for marine SINS using gravity in the inertial frame as a reference. IEEE/ION Position Location and Navigation Symposium, Monterey, California, USA, 961–965.Google Scholar
Han, S. and Wang, J. (2010). A novel inertial alignment scheme for low-cost INS aided by GPS for land vehicle application. Journal of Navigation, 63, 663–680.Google Scholar
Jamshaid, A. and Muhammad, U. (2009). A consistent and robust Kalman filter design for in-motion alignment of inertial navigation system. Measurement, 42, 577–582.Google Scholar
Joon, G.P., Jang, G.L. and Chan, G.P. (2004). SDINS/GPS in-flight alignment using GPS carrier phase rate. GPS Solutions, 8, 74–81.Google Scholar
Junkins, J.L. and Kim, Y. (1993). Introduction to Dynamics and Control of Flexible Structures, AIAA Education Series, AIAA, Washington, DC.Google Scholar
Kang, T.Z., Fang, J.C and Wang, W.G. (2012). Quaternion-Optimization-Based In-Flight Alignment Approach for Airborne POS. IEEE Transactions on Instrumentation and Measurement, 61, 2916–2923.Google Scholar
Kong, X., Nebot, E.M. and Durrant-Whyte, H. (1999). Development of a Nonlinear Psi-Angle Model for Large Misalignment Errors and Its Application in INS Alignment and Calibration. Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI.Google Scholar
Li, H., Pan, Q., Wang, X., Jiang, X. and Wang, J. (2015). Kalman filter design for initial precision alignment of a strapdown inertial navigation system on a rocking base. Journal of Navigation, 68, 184–195.Google Scholar
Li, J., Xu, J., Chang, L. and Feng, Z. (2014). An Improved Optimal Method for Initial Alignment. Journal of Navigation, 67, 727–736.Google Scholar
Li, W. and Wang, J. (2013). Effective adaptive Kalman Filter for MEMS- IMU/ Magnetometers Integrated Attitude and Heading Reference Systems. Journal of Navigation, 66, 99–113.Google Scholar
Li, W., Wang, J., Lu, L. and Wu, W. (2013). A novel Scheme for DVL-Aided SINS In-Motion alignment using UKF techniques. Sensors, 13, 1046–1063.Google Scholar
Lu, J., Xie, L. and Li, B. (2016). Applied Quaternion Optimization Method in Transfer Alignment for Airborne AHRS Under Large Misalignment Angle. IEEE Transactions on Instrumentation and Measurement, 65, 346–354.Google Scholar
Mei, C., Qin, Y. and Yang, P. (2015). Linear optimized self-alignment for SINS using Rodrigues parameters. Journal of Chinese Inertial Technology, 23, 298–302.Google Scholar
Qin, Y. (2014). Inertial Navigation, Science Press, Beijing, 2nd Ed. Google Scholar
Qin, Y., Zang, H. and Wang, S. (2014). Kalman filter and integrated navigation theory, Northwestern Polytechnical University, 3nd Ed. Google Scholar
Savage, P.G. (1998). Strapdown inertial navigation integration algorithm design, Part1: Attitude algorithms. Journal of Guidance, Control, and Dynamic, 21, 19–28.Google Scholar
Scherzinger, B.M. (1996). Inertial Navigation Error Models for Large Heading Uncertainty. IEEE Proceedings of PLANS, 447–484.Google Scholar
Shuster, M.D. and Oh, S.D. (1980). Three-Axis Attitude Determination from Vector Observations. Guidance and Control, 4, 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, 1930–1941.Google Scholar
Simon, D. (2006). Optimal State Estimation Kalman, H∞, and Nonlinear Approaches, John Wiley & Sons, Inc., Hoboken, New Jersey.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, 5, 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 System, 49, 1006–1023.Google Scholar
Yuan, D., Ma, X., Liu, Y and Zhang, C. (2016). Dynamic Inertial Alignment of the MEMS-based Low-cost SINS for AUV based on Unscented Kalman Filter. OCEANS 2016-Shanghai. IEEE.Google Scholar