Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
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Performance Analysis of Artificial Neural Network for Expanding the Ionospheric Correction Coverage of GNSS

위성항법시스템의 전리층 보정 가능 영역 확장을 위한 인공 신경망의 성능 분석

  • 류경돈 (과학기술연합대학원대학교 무기체계공학과) ;
  • 소형민 (국방과학연구소) ;
  • 박흥원 (국방과학연구소)
  • Received : 2018.10.08
  • Accepted : 2018.10.24
  • Published : 2018.10.31
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Abstract

Extrapolating the correction information of ionosphere is essential for expanding wide area differential GPS (WADGPS) service area beyond the reference station network. In this paper, design and analysis of the artificial neural network for expanding the ionospheric correction region will be proposed. First, analysis about influence of each input of neural network were performed. The inputs are the day/year periodic function, sunspot number, and geomagnetic index (Ap). Second, performance analysis with respect to the number of hidden layers and neurons in the neural network is shown. As a result, estimation of total electron contents (TEC) on the high/low latitude regions in solar max(2014) are displayed.

광역 차분위성항법시스템의 서비스 영역을 기준국 네트워크 외부로 확장하기 위해서는 전리층 보정 정보의 외삽이 필수적이다. 본 논문에서는 전리층 보정 영역 확장을 위한 인공 신경망을 설계하고 이에 대한 성능분석을 수행하였다. 인공 신경망 입력으로 사용되는 일/년별 주기함수, 태양흑점개수, 자기장 인덱스(Ap)의 개별 요소들이 전리층 외삽 추정 성능에 미치는 영향을 분석하였다. 신경망의 구성에 있어서는 은닉 층의 수 및 뉴런 개수 변화에 따른 성능 분석을 수행하였다. 분석결과를 바탕으로 신경망을 구현하고 태양활동 극대기(2014년)의 고위도와 저위도 지역에서의 전리층 추정 결과를 보였다.

Keywords

References

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