Journal of Aeronautical Engineering

Journal of Aeronautical Engineering

Position improvement of GPS/INS using NAR neural network and robust Kalman filter during GPS outage

Document Type : Original Article

Authors
Faculty of Electrical Engineering, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran
Abstract
Precise guidance and navigation is one of the necessities of every moving vehicle in the transportation industry. Different methods of navigation has been used to determine exact location of the vehicle in each moment. Inertial navigation is a newton-based method that provides position of the vehicle regardless of any external communication equipment. Inertial navigation is always subject to different disturbing errors that consistently reduce the performance of the system, therefore, for long-term navigation purposes, there should be at least one navigation assisting system to maintain positioning accuracy. Consequently, a GPS/INS data fusion using a Robust Extended Kalman Filter (REKF) is investigated in this paper. When vehicles enter an area with a signal jammer, GPS position would be unavailable, and, filter observations will not be updated. Thus, a trained nonlinear neural network is used to predict position in this scenario. In order to test the algorithm in real-world circumstances, a custom designed board with military standards is employed. The results show about 70% of position improvement towards each axis. The proposed algorithm has improved the position accuracy in GPS/INS integrated system in defined scenario.
Keywords

  • F. Abdel-Hafez, “The Autocovariance Least Squares Technique for GPS Interference/Jamming Detection.IFAC Proceedings, Vol. 41, no. 2, pp. 8990-8995, 2008.
  • S. Maybeck, "Stochastic models, estimation, and control, Vol. 3, Academic press, 1982.
  • Magnusson and T.Odenman “Improving absolute position of an automotive vehicle using GPS in sensor fusion,” Department of Signals and Systems, Chalmers University of Technology, 2012.
  • G. Lu, “Development of a GPS multi-antenna system for attitude determination,” Department of Geomatics, University of Calgary, 1995.
  • Nassar, “Improving the inertial navigation system (INS) error model for INS and INS/DGPS applications.” Department of Geomatics, University of Calgary, Engineering, 2003.
  • Duc-Tan, P. Fortier, and H.T. Huynh, “Design, simulation, and performance analysis of an INS/GPS system using parallel Kalman filters structure,” REV Journal on Electronics and Communications, Vol. 1, no. 2, pp. 88-96, 2011.
  • Y. Cho, and B.D. Kim, “Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system,” Automatica Vol. 44, no. 8, pp. 2040-2047, 2008.
  • Hu, S. Gao, Y. Zhong, “A derivative UKF for tightly coupled INS/GPS integrated navigation,”ISA transactions, Vol. 56, no. 1, pp. 135-144, 2015.
  • Ning, Yipeng, Jian Wang, Houzeng Han, Xinglong Tan, and Tianjun Liu. "An optimal radial basis function neural network enhanced adaptive robust Kalman filter for GNSS/INS integrated systems in complex urban areas." Sensors18, no. 9 (2018): 3091.
  • Zhang and B. Li, “A low-cost GPS/INS integration based on UKF and BP neural network,” Fifth International Conference on Intelligent Control and Information Processing, IEEE, pp. 100-107, Dalian, China, 2014.
  • Yao, X. Xu, C. Zhu, and C.Y. Chan, “A hybrid fusion algorithm for GPS/INS integration during GPS outages,” Measurement, Vol. 103, pp. 42-51, 2015.
  • Chen, C. Shen, W. Zhang, M. Tomizuka, Y. Xu, and K. Chiu, “Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages,” Measurement, Vol. 46, no. 10, pp. 3847-3854, 2013.
  • Gelb, Applied Optimal Estimation, Massachusetts Institute of Technology Press, 1974.
  • Fujita and N. Kanda, Physics of Leap Second, Department of Physics, Faculty of Science and Technology, 2009.
  • Xu and Y. Xu, GPS: theory, algorithms and applications, Springer, 2016.
  • Z. Sasiadek and P. Hartana, "GPS/INS Sensor fusion for accurate positioning and navigation based on Kalman Filtering," IFAC Proceedings. 37, pp. 115-20, 2004.
  • J. Woodman, “An introduction to inertial navigation.” No. UCAM-CL-TR-696, Computer Laboratory, University of Cambridge, 2007.
  • Titterton, David, John L. Weston, and John Weston. Strapdown inertial navigation technology. Vol. 17. IET, 2004.
  • O. Salytcheva, Medium accuracy INS/GPS integration in various GPS environments, University of Calgary, 2004.
  • El-Rabbany, “Introduction to GPS: the global positioning system.” Artech House Mobile Communications Series, 2nd Edition, Boston, 2002.
  • Quinchia A.G., Falco G., Falletti E., Dovis F., Ferrer C., "A comparison between different error modelling of MEMS applied to GPS/INS integrated systems," Sensors, 13, pp. 9549–9588, 2013.
  • Wang J., Han H., Meng X., Li Z., "Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages," Survey Review, 49(357), pp. 419-427, 2017.
  • Zhang, Yuexin. "A fusion methodology to bridge GPS outages for INS/GPS integrated navigation system." IEEE Access7 (2019): 61296-61306.
  • Bai, Yuting, Baihai Zhang, Senchun Chai, Xuebo Jin, Xiaoyi Wang, and Tingli Su. "Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network." In 2018 37th Chinese Control Conference (CCC), pp. 4179-4184. IEEE, 2018.
  • Sukkarieh, Low cost, high integrity, aided inertial navigation systems for autonomous land vehicles, Department of Mechanical and Mechatronic Engineering, University of Sydney, 2000.
  • Li, Xu, Wei Chen, Chingyao Chan, Bin Li, and Xianghui Song. "Multi-sensor fusion methodology for enhanced land vehicle positioning." Information Fusion46 (2019): 51-62.
  • T. Pham and B.S. Yang, “A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting,” Expert Systems with Applications, Vol. 37, no. 4, pp. 3310-3317, 2010.
Volume 21, Issue 2
December 2019
Pages 46-56

  • Receive Date 13 January 2021