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An improved method using adaptive smoothing for GNSS tomographic imaging of ionosphere.
Jia, Rushang; Yu, Xumin; Xing, Jianping; Ning, Yafei; Sun, Hecheng.
Afiliación
  • Jia R; School of Microelectronics, Shandong University, Jinan, China.
  • Yu X; Shanghai Key Laboratory of Space Navigation and Position Techniques, Shanghai, China.
  • Xing J; National Key Lab. of Science and Technology on Space Microwave China Academy of Space Technology Xi'an, Xi'an, China.
  • Ning Y; School of Microelectronics, Shandong University, Jinan, China.
  • Sun H; School of Microelectronics, Shandong University, Jinan, China.
PLoS One ; 16(5): e0250613, 2021.
Article en En | MEDLINE | ID: mdl-33961638
ABSTRACT
Global navigation satellite system (GNSS) is a well-established sensors in the recent ionosphere research. By comparing with classical meteorological equipments, the GNSS application can obtain more reliable and precious ionospheric total electron content (TEC) result. However, the most used GNSS ionospheric tomography technique is sensitive to a priori information due to the sparse and non-uniform distribution of GNSS stations. In this paper, we propose an improved method based on adaptive Laplacian smoothing and algebraic reconstruction technique (ALS-ART). Compared with traditional constant constraints, this method is less dependent on a priori information and adaptive smoothing constraints is closer to the actual situation. Tomography experiments using simulated data show that reconstruction accuracy of ionospheric electron density using ALS-ART method is significantly improved. We also use the method to do the analysis of real observation data and compare the tomography results with ionosonde observation data. The results demonstrate the superiority and reliability of the proposed method compared to traditional constant constraints method which will further improve the capability of obtaining precious ionosphere TEC by using GNSS.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Atmósfera / Algoritmos / Aumento de la Imagen / Sistemas de Información Geográfica / Ionización del Aire / Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2021 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Atmósfera / Algoritmos / Aumento de la Imagen / Sistemas de Información Geográfica / Ionización del Aire / Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2021 Tipo del documento: Article País de afiliación: China
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