International Journal of Aeronautical and Space Sciences

  • 제17권1호
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  • Pages.64-72
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  • 2016
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  • 2093-274X(pISSN)
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  • 2093-2480(eISSN)
한국항공우주학회 (The Korean Society for Aeronautical and Space Sciences)
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Extending Ionospheric Correction Coverage Area By Using A Neural Network Method

  • Kim, Mingyu (School of Aerospace and Mechanical Engineering, Korea Aerospace University) ;
  • Kim, Jeongrae (School of Aerospace and Mechanical Engineering, Korea Aerospace University)
  • 투고 : 2015.07.27
  • 심사 : 2016.02.24
  • 발행 : 2016.03.30
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초록

The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which combines observed ionospheric delay with the environmental parameters, is proposed. Neural network and least square regression algorithms are developed to utilize the combined input data. The bi-harmonic spline method is also tested for comparison. The IGS ionosphere map data is used to simulate the delays and to compute the extrapolation error statistics. The neural network method outperforms the other methods and demonstrates a high extrapolation accuracy. In order to determine the directional characteristics, the estimation error is classified into four direction components. The South extrapolation area yields the largest estimation error followed by North area, which yields the second-largest error.

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참고문헌

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피인용 문헌

  1. Extending the coverage area of regional ionosphere maps using a support vector machine algorithm vol.37, pp.1, 2019, https://doi.org/10.5194/angeo-37-77-2019