In GNSS navigation and positioning, ionospheric delay error is one of the sources of error that cannot be ignored. The BDS Klobuchar (BDSKlob) and BDGIM model parameters are broadcast by the BDS Navigation Satellite System in the broadcast ephemeris to correct for ionospheric delay errors, which can meet the basic navigation and positioning needs of users. However, in the face of the growing demand for autonomous positioning and navigation accuracy, it is necessary to further improve the accuracy of the model and reduce the impact of the space environment on positioning. In this paper, a Back Propagation (BP) neural network optimized by Artificial Bee Colony Algorithm (ABC) is used to compensate for the error prediction of the BDS broadcast ionosphere model from 7 to 13 September 2021. For BDSKlob and BDGIM, a number of grid points in the Chinese region and worldwide are selected for experimental analysis. BDSKlob and BDGIM respectively selected several grid points in China and the world for experimental analysis. The results show that the prediction compensation of the BDS broadcast ionosphere model errors using ABC-BP neural network can achieve better accuracy results. For BDSKlob, the model correction rate improved to 81.66% in China after using the predicted values to compensate for the model values. For BDGIM, the accuracy was significantly improved in the global mid and high latitudes, with model correction rates of 74.25%, 82.05% and 82.13% for the high, mid and low latitudes respectively after compensation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Accuracy Analysis of Error Compensation in the Ionospheric Model of BDS Broadcasting Based on ABC-BP Neural Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yang, Changfeng (editor) / Xie, Jun (editor) / Li, Qiang (author) / Su, Xing (author) / Xie, Xin (author) / Tao, Chunyan (author) / Cui, Jianhui (author) / Chen, Hanlin (author) / Liu, Zhimin (author)


    Publication date :

    2022-05-05


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    A neural network-based ionospheric model for Arecibo

    Friedrich, M. / Fankhauser, M. / Oyeyemi, E. et al. | Elsevier | 2007


    A neural network-based ionospheric model for Arecibo

    Friedrich, M. | Online Contents | 2008



    Robot Error Compensation using a Localised Neural Model

    Jenkinson, I. D. / Montgomery, P. A. / Mills, B. | British Library Conference Proceedings | 1995