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Beidou navigation method based on intelligent computing and extended Kalman filter fusion

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Abstract

Scientific and precise dynamic navigation is the key to improving Beidou positional accuracy of agricultural machinery. Aiming at the gross error of agricultural machinery location. First, the paper deeply explores the principle of Beidou navigation. Second, according to the PVT information (position, velocity and time) solutions of Beidou navigation system, the least square method, Kalman filter method and extended Kalman filter method were studied. Based on their own advantages, algorithms were proposed that combines the differential adaptation and extended Kalman filter. Then, based on the equivalent gain matrix and iterative solution, a robust adaptive Kalman filter model is built to verify its effectiveness in reducing gross errors. At last, the four algorithms were simulated in MATLAB and the simulation results were compared to verify that the newly-proposed method is the optimal solution algorithm. The absolute error remained 5.2 cm, meeting the preciseness limit of the agricultural machinery navigation.

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Acknowledgements

This paper was supported by the National key R & D plan (Grant: 2017YFD0710201&2016YFD0702103), Shandong province natural science foundation of China (Grant: 2017CXGC0903&2018CXGC0214), Shandong agricultural machinery innovation plan (Grant: 2017YF006-02&2018YZ002).

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Yongwei Tang, Maoli Wang, Huijuan Hao & Yuxiao Meng

  2. School of Automation Engineering, Qingdao University of Technology, Qingdao, China

    Jingbo Zhao

  3. College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, China

    Xiaoning He

  4. School of economics and management, University of Electronic Science and Technology of China, Chengdu, China

    Yuxiao Meng

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  1. Yongwei Tang
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  2. Jingbo Zhao
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  3. Maoli Wang
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  4. Huijuan Hao
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  5. Xiaoning He
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  6. Yuxiao Meng
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Correspondence to Maoli Wang.

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Tang, Y., Zhao, J., Wang, M. et al. Beidou navigation method based on intelligent computing and extended Kalman filter fusion. J Ambient Intell Human Comput 10, 4431–4438 (2019). https://doi.org/10.1007/s12652-018-1124-5

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  • DOI: https://doi.org/10.1007/s12652-018-1124-5

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