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Optimisation-based Transfer Alignment and Calibration Method for Inertial Measurement Vector Integration Matching

Published online by Cambridge University Press:  20 October 2016

Lili Xie
Affiliation:
(Beihang University, 100191 Beijing, People's Republic of China)
Jiazhen Lu*
Affiliation:
(Beihang University, 100191 Beijing, People's Republic of China)
*
(E-mail: ljzbuaa@163.com)

Abstract

The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Chang, L.B., Li, J.S., and Chen, S.Y. (2015a). Initial alignment by attitude estimation for strapdown inertial navigation systems. IEEE Transactions on Instrumentation & Measurement, 64, 784794.Google Scholar
Chang, L.B., Hu, B.Q., and Li, Y. (2015b). Backtracking integration for fast attitude determination-based initial alignment. IEEE Transactions on Instrumentation & Measurement, 64, 795803.Google Scholar
Chattaraj, S., Mukherjee, A., and Chaudhuri, S.K. (2013). Transfer alignment problem: Algorithms and design issues. Gyroscopy and Navigation, 4, 130146.Google Scholar
Groves, P.D. (2008). Principles of GNSS, Inertial, and Multisensor Integrated Navigation System , Artech House, Boston and London.Google Scholar
Kang, T.Z., Fang, J.C., and Wang, W. (2012). Quaternion-Optimization-Based In-Flight Alignment Approach for Airborne POS. IEEE Transactions on Instrumentation and Measurement, 61, 29162923.Google Scholar
Li, J.S., Xu, J.N., Chang, L.B., and Zha, F. (2014). An improved optimal method for initial alignment. Journal of Navigation, 67, 727736.Google Scholar
Silson, P.M.G. (2011). Coarse Alignment of a Ship's Strapdown Inertial Attitude Reference System Using Velocity Loci. IEEE transaction on instrument and measurement, 60, 19301941.Google Scholar
Titterton, D.H., and Weston, J.L. (2004). Strapdown inertial navigation technology , 2nd ed., IEE, Herts, U.K. Google Scholar
Wu, M.P., Wu, Y.X., Hu, X.P. and Hu, D.W. (2011). Optimization-based alignment for inertial navigation systems: Theory and algorithm. Aerospace Science and Technology, 15, 117.Google Scholar
Wu, Y.X. and Pan, X.F. (2013). Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment. IEEE Transactions on Aerospace and Electronic Systems, 49, 10061023.Google Scholar
Wu, Y.X., Wang, J.L., and Hu, D.W. (2014). A New Technique for INS/GNSS Attitude and Parameter Estimation Using Online Optimization. IEEE Transactions on Signal Processing, 62, 26422655.Google Scholar