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Enhanced Kalman Filter using Noisy Input Gaussian Process Regression for Bridging GPS Outages in a POS

Published online by Cambridge University Press:  28 November 2017

Wen Ye ,
Zhanchao Liu Open the ORCID record for Zhanchao Liu [Opens in a new window] ,
Chi Li  and
Jiancheng Fang
Wen Ye*
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
Zhanchao Liu
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
Chi Li
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
Jiancheng Fang
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China)
*
(E-mail: wenye@buaa.edu.cn)
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Abstract

A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.

Keywords

Position and Orientation SystemKalman FilterGPS outagesNoisy Input Gaussian Process RegressionHybrid predictorTime series
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 565 - 584
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Atia, M.M., Noureldin, A. and Korenberg, M. (2011). Gaussian process regression approach for bridging GPS outages in integrated navigation systems. Electronics Letters, 47(1), 52–53.Google Scholar
Chatfield, A.B. (1997). Fundamentals of High Accuracy Inertial Navigation. American Institute of Aeronautics and Astronautics: Reston, VA, USA.Google Scholar
Chen, H., Cheng, X., Wang, H. and Xu, H. (2014). Dealing with observation outages within navigation data using Gaussian process regression. Journal of Navigation, 67(4), 603–615.Google Scholar
Chen, L. and Fang, J. (2014). A hybrid prediction method for bridging gps outages in high-precision pos application. IEEE Transactions on Instrumentation & Measurement, 63(6), 1656–1665.Google Scholar
Chen, S., Ma, H., Zhang, Y., Liang, Z., Xu, J. and He, C. (2011). Boresight calibration of airborne lidar system without ground control points. IEEE Geoscience & Remote Sensing Letters, 9(1), 85–89.Google Scholar
Grewal, M., Weill, L. and Andrews, A. (2007). Global Positioning Systems, Inertial Navigation, and Integration, with MATLAB®CD. (Vol. 46, pp.23–39). Springer New York.Google Scholar
Grimes, D.B., Chalodhorn, R. and Rao, R.P.N. (2006). Dynamic Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference. In: Proceedings of Robotics: Science and Systems, Philadelphia, USA.Google Scholar
Groves, P.D. (2013). Principles of GNSS, inertial and multisensor integrated navigation systems (Second edition). Boston, [Massachusetts] Artech House.Google Scholar
Hasan, A.M., Samsudin, K., Ramli, A.R. and Azmir, R. S. (2011). Automatic estimation of inertial navigation system errors for global positioning system outage recovery. Proceedings of the Institution of Mechanical Engineers Part G, Journal of Aerospace Engineering, 1(G1), 1–11.Google Scholar
Ip, A., Elsheimy, N. and Mostafa, M. (2007). Performance analysis of integrated sensor orientation. Photogrammetric Engineering & Remote Sensing, 73(1), 89–97.Google Scholar
Jwo, D.J. and Chen, J.J. (2007). Neural network aided adaptive Kalman filter for GPS/INS navigation system design. Proceedings of the 9th IFAC Workshop, 1–7.Google Scholar
Jwo, D.J. and Huang, H.C. (2004). Neural network aided adaptive extended Kalman filtering approach for DGPS positioning. Journal of Navigation, 57(3), 449–463.Google Scholar
Ko, J., Klein, D.J., Fox, D. and Haehnel, D. (2007). Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp. IEEE International Conference on Robotics and Automation, 742–747.Google Scholar
Liu, J.D., Wei, E. and Jin, S.G. (2017). Cruise orbit determination of Mars from combined optical celestial techniques and X-ray pulsars. Journal of Navigation, 70(4), 719–734.Google Scholar
Mokhtari, A. and Ribeiro, A. (2014). Global convergence of online limited memory BFGS. Journal of Machine Learning Research, 16, 3151–3181.Google Scholar
Mostafa, M.M.R., Hutton, J. (2001): Direct Positioning and Orientation Systems - How Do They Work? What is The Attainable Accuracy? Proceedings of the American Society of Photogrammetry and Remote Sensing (ASPRS) Annual Meeting, ST. Louis, MO, USA, April 22–26, 1–11.Google Scholar
Nassar, S.M. and Schwartz, K.P. (2001). Bridging DGPS Outages in Kinematic Applications Using A Simple Algorithm for INS Bias Modeling. The International Symposium on Kinematic Systems in Geodesy, Geomatics and Navigation. 401–408Google Scholar
Rasmussen, C.E. and Williams, C.K.I. (2005). Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press.Google Scholar
Roberts, S., Osborne, M., Ebden, M., Reece, S., Gibson, N. and Aigrain, S. (2012). Gaussian processes for time-series modelling. Philosophical Transactions, 371(1984), 20110550.Google Scholar
Tan, X., Wang, J., Jin, S.G. and Meng, X. (2015). GA-SVR and pseudo-position-aided GPS/INS integration during GPS outages. Journal of Navigation, 68(4), 678–696.Google Scholar
Toth, C. K. (2001). Sensor integration in airborne mapping. IEEE Transactions on Instrumentation & Measurement, 51(6), 1367–1373.Google Scholar
Wang, J. J., Wang, J., Sinclair, D. and Watts, L. (2006) A Neural Network and Kalman Filter Hybrid Approach for GPS/INS Integration. In: Papers presented at the IAIN/GNSS 2006 Conference, October 18–20, Jeju Island, South Korea, Vol. 1, 277–282.Google Scholar
Wang, J.J., Wang, J., Sinclair, D. and Watts, L. (2008). Neural network aided Kalman filtering for integrated GPS/INS geo-referencing platform. Toronto Journal of Theology, 30, 257–268.Google Scholar
Wei, E., Jin, S., Yang, H., Li, X., Gu, H. & Li, Z., et al. (2013). Simulation and results on real-time positioning of chang'e-3 rover with the same-beam vlbi observations. Planetary & Space Science, 84, 20–27.Google Scholar
Xu, Z., Li, Y., Rizos, C. and Xu, X. (2010). Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration. Journal of Navigation, 63(2), 289–299.Google Scholar
Zhang, L., Qiao, Z., Xing, M.D., Yang, L. and Bao, Z. (2012). A robust motion compensation approach for UAV SAR imagery. IEEE Transactions on Geoscience & Remote Sensing, 50(8), 3202–3218.Google Scholar
Zhu, Z. and Li, C. (2016). Study on real-time identification of GNSS multipath errors and its application. Aerospace Science & Technology, 52, 215–223.Google Scholar