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A DDF-based IMM-TFS Approach for the Accuracy Evaluation Problem of Rapid Transfer Alignment

Published online by Cambridge University Press:  21 January 2018

Dapeng Zhou  and
Lei Guo
Dapeng Zhou*
Affiliation:
(School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China)
Lei Guo
Affiliation:
(Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, China)
*
(E-mail: [email protected])
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Abstract

This study aims to address the accuracy evaluation problem for rapid transfer alignment with the coexistence of large misalignment angles and uncertain observation noises. For the requirement of accuracy evaluation, complete information in terms of misalignment angles should be estimated during the alignment process. Thus, a fixed-interval smoothing approach is the core of solving this problem. In this paper, a new Divided Difference Filter (DDF)-based an Interacting Multiple Model Two-Filter Smoother (IMM-TFS) is developed to estimate the misalignment angles. The proposed DDF-based IMM-TFS releases the restriction of inverse nonlinearity by using the weighted statistical linearization regression method, and the resulting pseudo-linear model can be used for backward-time IMM filtering. The smoothing step takes into account the merging of estimations and the interaction of multiple models simultaneously. The new smoother is compared with the previous well-known methodologies in simulations. The results show that the DDF-based IMM-TFS can achieve better accuracy for misalignment angles estimation, and has a high efficiency for detecting the changes in a model.

Keywords

Rapid transfer alignmentAccuracy evaluationDivided difference Filter (DDF)Interacting Multiple Model Two-Filter Smoother (IMM-TFS)
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 749 - 768
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Blom, H.A.P. and Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE transactions on Automatic Control, 33(8), 780–783.Google Scholar
Brunke, S. and Campbell, M. (2004). Square root sigma point filtering for real-time, nonlinear estimation. Journal of Guidance Control and Dynamics, 27(2), 314–317.Google Scholar
Gong, X.L. and Qin, T.T. (2014). Airborne Earth Observation Positioning and Orientation by SINS/GPS Integration Using CD R-T-S Smoothing. The Journal of Navigation, 67(2), 211–225.Google Scholar
Gong, X.L., Zhang, J.X. and Fang, J.C. (2015). A modified nonlinear two-filter smoothing for high-precision airborne integrated GPS and inertial navigation. IEEE Transactions on Instrumentation and Measurement, 64(12), 3315–3322.Google Scholar
Helmick, R.E., Blair, W.D., Hoffman, S.A. and Hoffman, S. (1995). A Fixed-interval smoothing for Markovian switching systems. IEEE Transactions on Information Theory, 41(6), 1845–1855.CrossRefGoogle Scholar
Kain, J.E. and Cloutier, J.R. (1989). Rapid transfer alignment for tactical weapon applications. AIAA Guidance, Navigation and Control Conference, 1290–1300.Google Scholar
Lim, Y.C. and Lyou, J. (2001). An error compensation method for transfer alignment. IEEE Region 10 International Conference on Electrical and Electronic Technology, Singapore, Vol 2, 850–855.Google Scholar
Liu, H., Nassar, S. and El-Sheimy, N. (2010). Two-Filter Smoothing for Accurate INS/GPS Land-Vehicle Navigation in Urban Centers. IEEE Transactions on Vehicular Technology, 59(9), 4256–4267.Google Scholar
Malleswaran, M., Vaidehi, V., Irwin, S. and Robin, B. (2013). IMM-UKF-TFS model-based Approach for intelligent navigation. Journal of Navigation, 66(6), 859–877.CrossRefGoogle Scholar
Mazor, E., Averbuch, A., Bar-Shalom, Y. and Dayan, J. (1998). Interacting multiple model methods in target tracking: a survey. IEEE Transactions on aerospace and electronic systems, 34(1), 103–123.Google Scholar
Nadarajah, N., Tharmarasa, R., McDonald, M. and Kirubarajan, T. (2012). IMM forward filtering and backward smoothing for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems, 48(48), 2673–2678.Google Scholar
Nørgaard, M., Poulsen, N.K. and Ravn, O. (2000). New developments in state estimation for nonlinear systems. Automatica, 36(11), 1627–1638.Google Scholar
Paul, A.S. and Wan, E.A. (2008). A new formulation for nonlinear forward-backward smoothing. IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, USA, 3621–3624.CrossRefGoogle Scholar
Pehlivanoǧlu, A.G. and Ercan, Y. (2013). Investigation of flexure effect on transfer alignment performance. Journal of Navigation, 66(1), 1–15.Google Scholar
Särkkä, S. (2013). Bayesian filtering and smoothing. Cambridge University Press.CrossRefGoogle Scholar
Simon, D. (2006). Optimal state estimation: Kalman, H∞, and nonlinear approaches. John Wiley & Sons.Google Scholar
Wang, B., Xiao, X., Xia, Y.Q. and Fu, M.Y. (2013). Unscented particle filtering for estimation of shipboard deformation based on inertial measurement units. Sensors, 13(11), 15656–15672.Google Scholar
Wei, X.T. and Gao, L. (2012). Transfer alignment of carrier-born aircraft under large azimuth misalignment angle. Journal of Chinese Inertial Technology, 20(5), 552–556.Google Scholar
Yang, G.L., Wang, Y.Y. and Yang, S.J. (2014). Assessment approach for calculating transfer alignment accuracy of SINS on moving base. Measurement, 52(6), 55–63.Google Scholar