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A Robust Vehicle Positioning Method Based on Low-Cost Sensors Under Short-Term Failure of Global Positioning System
Technical Paper
2021-01-7010
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Combining the advantages of GPS and INS, GPS/INS has been widely used in vehicle localization. However, there are GPS failures in actual scenes, and even a short-term failure will cause the positioning accuracy of the low-cost MEMS-SINS to decline rapidly. Therefore, aiming at the problem of vehicle positioning under short-term GPS failure, the following three aspects of work are mainly carried out. Firstly, the unscented quaternion estimator is introduced, and the velocity-combined GPS/SINS based on this algorithm is realized. Secondly, for the positioning problem under short-term GPS failure, a USQUE-LSTM positioning method is proposed by introducing the long short-term memory neural network, which is the main contribution of this paper. Finally, considering the defects of the neural network, in order to improve the adaptability of the positioning strategy to high-speed obstacle avoidance and other scenes, the USQUE-VDM positioning method is designed by introducing the two-degree-of-freedom dynamic model of vehicle; then based on the federated Kalman filter, the USQUE-VDM and USQUE-LSTM are further integrated, and the USQUE-LSTM-VDM positioning method under GPS short-term failure is proposed. To verify the proposed positioning methods, real vehicle tests and CarSim + MATLAB/Simulink co-simulation are carried out. The results show that the proposed USQUE-LSTM method can effectively improve the vehicle positioning performance under short-term GPS failure, and the positioning root-mean-squared error is less than 5m within 60s; and the proposed USQUE-LSTM-VDM strategy has higher positioning accuracy and better scene adaptability, which provides an effective solution to the positioning problem under short-term GPS failure.
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Citation
Wang, Y., Zhao, Z., and Liang, K., "A Robust Vehicle Positioning Method Based on Low-Cost Sensors Under Short-Term Failure of Global Positioning System," SAE Technical Paper 2021-01-7010, 2021, https://doi.org/10.4271/2021-01-7010.Also In
References
- Li , D. , Li , Q. , Tang , L. , Yang , S. et al. Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors Micromachines 6 4 Apr. 2015 487 522 10.3390/mi6040487
- Chang , L. , Hu , B. , Chang , G. , and Li , A. Huber-Based Novel Robust Unscented Kalman Filter IET Science, Measurement & Technology 6 6 2012 502 509 10.1049/iet-smt.2011.0169
- Feng , K. , Li , J. , Zhang , X. , Shen , C. et al. An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems Sensors 18 6 2018 1919 10.3390/s18061919
- Crassidis , J.L. and Markley , F.L. Unscented Filtering for Spacecraft Attitude Estimation Journal of Guidance, Control, and Dynamics 26 4 2003 536 542 10.2514/2.5102
- Skog , I. and Handel , P. In-Car Positioning and Navigation Technologies—A Survey IEEE Trans. Intell. Transp. Syst. 10 1 Mar. 2009 4 21 10.1109/TITS.2008.2011712
- Min , H. , Wu , X. , Cheng , C. , and Zhao , X. Kinematic and Dynamic Vehicle Model-Assisted Global Positioning Method for Autonomous Vehicles with Low-Cost GPS/Camera/In-Vehicle Sensors Sensors (Basel, Switzerland) 19 24 2019 10.3390/s19245430
- Lin , X. , and He , Y. Failure Detection Method of Double Satellite Positioning in its Integrated System with SINS Acta Geodaetica et Cartographica Sinica 35 4 332 336 November 2006
- Zhang , M. , Li , K. , Hu , B. , and Lü , X. Strapdown Inertial Integrated Navigation Algorithm Under Short-Term Failure of Information Sources Acta Armamentarii 41 10 2008 2015 October 2020 10.3969/j.issn.1000-1093.2020.10.010
- Malleswaran , M. , Vaidehi , V. , and Mohankumar , M. A Hybrid Approach for GPS/INS Integration Using Kalman Filter and IDNN 2011 Third International Conference on Advanced Computing IEEE 378 383 2011 10.1109/ICoAC.2011.6165205
- Belhajem , I. , Maissa , Y.B. , and Tamtaoui , A. Improving Low Cost Sensor Based Vehicle Positioning with Machine Learning Control Engineering Practice 74 2018 168 176 10.1016/j.conengprac.2018.03.006
- Li , J. , and Wu , Y. Realization of GNSS/INS Tightly Coupled Navigation and Reliability Verification in Intelligent Driving System 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) IEEE 838 842 2018 10.1109/CYBER.2018.8688120
- Lu , S. , Gong , Y. , Luo , H. , Zhao , F. et al. Heterogeneous Multi-Task Learning for Multiple Pseudo-Measurement Estimation to Bridge GPS Outages IEEE Transactions on Instrumentation and Measurement 2020 10.1109/TIM.2020.3028438
- Fang , W. , Jiang , J. , Lu , S. , Gong , Y. et al. A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS During GNSS Signal Outages Remote Sensing 12 2 2020 256 10.3390/rs12020256
- Noureldin , A. , Karamat , T.B. , and Georgy , J. Fundamentals of Inertial Navigation, Satellite-Based Positioning and Their Integration Springer Science & Business Media 2012 10.1007/978-3-642-30466-8
- Li , K. , Hu , B. , and Chang , L. Modified Quaternion Unscented Kalman Filter Systems Engineering and Electronics 38 6 1399 404 June 2016 10.3969/j.issn.1001-506X.2016.06.28
- Chen , H. , Yin , D. , and Zhang , Q. Analysis and Verification on Improving MEMS Navigation Accuracy Based on LSTM Network Journal of Chinese Inertial Technology 26 5 610 615 October 1, 2018 10.13695/j.cnki.12-1222/o3.2018.05.009