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Fu Shuaizhi, Chen Wei, Wu Di, Kong Haiyang, Zheng Hongjiang, Du Luyao. A GNSS/INS Vehicle Integrated Navigation System Based on LSTM-EKF[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200115
Citation: Fu Shuaizhi, Chen Wei, Wu Di, Kong Haiyang, Zheng Hongjiang, Du Luyao. A GNSS/INS Vehicle Integrated Navigation System Based on LSTM-EKF[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200115

A GNSS/INS Vehicle Integrated Navigation System Based on LSTM-EKF

doi: 10.13203/j.whugis20200115
Funds:

National Key R&D Program of China (2018YFB0105205)

  • Received Date: 2020-05-25
    Available Online: 2021-05-07
  • Integration of GNSS and INS can provide continuous and accurate positioning information for vehicles. However, the accuracy of low-cost GNSS/INS vehicle integrated navigation systems is unreliable during GNSS outages, which are common in urban areas. So, a long and short-term memory (LSTM) networks-aided GNSS/INS integrated navigation system based on extended Kalman filter (EKF) is proposed in this paper. LSTM networks are trained to learn the relationship between position error and INS output when GNSS available. When GNSS outage occurs, LSTM networks predict and correct errors of the integrated navigation system to improve location precision. The experiment shows that the north error and east error of the GNSS/INS integrated navigation systems based on EKF is 1.93m and 13.92m during the 15s GNSS outage. Meanwhile, the north error and east error of the GNSS/INS integrated navigation systems based on LSTM-EKF is 1.17m and 0.84m. The comparison results indicate that the proposed system can effectively improve location precision during GNSS outages.
  • [1] Renfro B A, Stein M, Boeker N, et al. An analysis of global positioning system (GPS) standard positioning service (SPS) performance for 2017[J]. See https://www.gps.gov/systems/gps/performance/2014-GPS-SPS-performance-analysis.pdf, 2018.
    [2] Sasani S, Asgari J, Amiri-Simkooei A R. Improving MEMS-IMU/GPS Integrated Systems for Land Vehicle Navigation Applications[J]. GPS solutions, 2016, 20(1):89-100.
    [3] Tan X, Wang J, Jin S, et al. GA-SVR and Pseudo-Position-Aided GPS/INS Integration during GPS Outage[J]. The Journal of Navigation, 2015, 68(4):678-696.
    [4] Yao Y, Xu X. A RLS-SVM Aided Fusion Methodology for INS during GPS Outages[J]. Sensors, 2017, 17(3):432-445.
    [5] Belhajem I, Maissa Y B, Tamtaoui A. A Hybrid Low Cost Approach using Extended Kalman Filter and Neural Networks for Real time Positioning[C]. 2016 International Conference on Information Technology for Organizations Development (IT4OD). IEEE, 2016:1-5.
    [6] Clark B J, Simmons C M, Berkowitz L E, et al. The retrosplenial-parietal network and reference frame coordination for spatial navigation[J]. Behavioral neuroscience, 2018, 132(5):416.
    [7] Barrau A, Bonnabel S. The Invariant Extended Kalman Filter as A Stable Observer[J]. IEEE Transactions on Automatic Control, 2016, 62(4):1797-1812.
    [8] Ko N Y, Youn W, Choi I H, et al. Features of invariant extended Kalman filter applied to unmanned aerial vehicle navigation[J]. Sensors, 2018, 18(9):2855.
    [9] Groves P D. Navigation using Inertial Sensors[Tutorial] [J]. IEEE Aerospace and Electronic Systems Magazine, 2015, 30(2):42-69.
    [10] Li X, Wang Y, Khoshelham K. Comparative analysis of robust extended Kalman filter and incremental smoothing for UWB/PDR fusion positioning in NLOS environments[J]. Acta Geodaetica et Geophysica, 2019, 54(2):157-179.
    [11] Jozefowicz R, Zaremba W, Sutskever I. An Empirical Exploration of Recurrent Network Architectures[C]. International conference on machine learning. 2015:2342-2350.
    [12] El-Mowafy A. Analysis of Web-based GNSS Post-Processing Services for Static and Kinematic Positioning using Short Data Spans[J]. Survey review, 2011, 43(323):535-549.
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A GNSS/INS Vehicle Integrated Navigation System Based on LSTM-EKF

doi: 10.13203/j.whugis20200115
Funds:

National Key R&D Program of China (2018YFB0105205)

Abstract: Integration of GNSS and INS can provide continuous and accurate positioning information for vehicles. However, the accuracy of low-cost GNSS/INS vehicle integrated navigation systems is unreliable during GNSS outages, which are common in urban areas. So, a long and short-term memory (LSTM) networks-aided GNSS/INS integrated navigation system based on extended Kalman filter (EKF) is proposed in this paper. LSTM networks are trained to learn the relationship between position error and INS output when GNSS available. When GNSS outage occurs, LSTM networks predict and correct errors of the integrated navigation system to improve location precision. The experiment shows that the north error and east error of the GNSS/INS integrated navigation systems based on EKF is 1.93m and 13.92m during the 15s GNSS outage. Meanwhile, the north error and east error of the GNSS/INS integrated navigation systems based on LSTM-EKF is 1.17m and 0.84m. The comparison results indicate that the proposed system can effectively improve location precision during GNSS outages.

Fu Shuaizhi, Chen Wei, Wu Di, Kong Haiyang, Zheng Hongjiang, Du Luyao. A GNSS/INS Vehicle Integrated Navigation System Based on LSTM-EKF[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200115
Citation: Fu Shuaizhi, Chen Wei, Wu Di, Kong Haiyang, Zheng Hongjiang, Du Luyao. A GNSS/INS Vehicle Integrated Navigation System Based on LSTM-EKF[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200115
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