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Integrated Navigation on Vehicle Based on Low-cost SINS/GNSS Using Deep Learning

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Abstract

The high-precision integrated navigation time for the MEMS SINS/GNSS combination system is too long, and it is difficult to ensure the flexible navigation of existing low-cost vehicles. A deep learning assisted integrated navigation method is proposed, which uses MEMS gyroscopes, accelerometers and dual-antenna satellite receivers to achieve fast alignment under MEMS SINS/GNSS. Based on the analysis of traditional coordinates and strapdown inertial navigation, the dynamic error model is given. The integrated navigation frame of CNN-LSTM is proposed. At the same time, the EKF filter for training is designed and the motion characteristics of the car body are selected. The state equations and observations, using the EKF to train the CNN-LSTM model, ultimately achieve integrated navigation. Finally, the proposed method is simulated and tested. The final attitude accuracy is better than 0.2°, the alignment time is 10 s and position accuracy is better than 3 m. Compared with the EKF navigation method, the navigation accuracy and the alignment time are significantly improved, which can meet the requirements of low-cost vehicle flexibility.

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Acknowledgements

This work was supported by the Beijing Natural Science Foundation (4212003), the National Natural Science Foundation of China (61801032), National Key Research and Development Project (2020YFC1511702), the Beijing Key Laboratory of High Dynamic Navigation Technology, Laboratory of Modern Measurement & Control Technology (Beijing Information Science & Technology University Ministry of Education), and Qin Xin Talents Cultivation Program, Beijing Information Science & Technology University.

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Authors and Affiliations

  1. Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technological University, Beijing, 100101, China

    Ning Liu, Zhao Hui, Zhong Su, Likang Qiao & Yiping Dong

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  1. Ning Liu
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  2. Zhao Hui
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  3. Zhong Su
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  4. Likang Qiao
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  5. Yiping Dong
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Correspondence to Ning Liu.

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Liu, N., Hui, Z., Su, Z. et al. Integrated Navigation on Vehicle Based on Low-cost SINS/GNSS Using Deep Learning. Wireless Pers Commun 126, 2043–2064 (2022). https://doi.org/10.1007/s11277-021-08758-9

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