利用COSMIC-2 GNSS无线电掩星和人工神经网络对埃及上空电离层TEC进行建模

IF 1.2 Q4 REMOTE SENSING Journal of Applied Geodesy Pub Date : 2023-10-27 DOI:10.1515/jag-2023-0079
Ahmed Sherif, Mostafa Rabah, Ashraf El-Kutb Mousa, Ahmed Zaki, Mohamed Anwar, Ahmed Sedeek
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引用次数: 0

摘要

电离层延迟严重影响GNSS定位精度。为了解决这一问题,利用COSMIC-2高质量电离层剖面数据集开发了人工神经网络(ANN)来预测总电子含量(TEC)。人工神经网络擅长处理线性和非线性挑战。在本研究中,培养了8种不同的人工神经网络。这些人工神经网络设计了以下输入年、月、日、时、纬度和经度。以及太阳和地磁参数,如F10.7太阳射电通量指数、太阳黑子数(SSN)、Kp指数和ap指数。目的是找出对电离层预测影响最大的参数。在确定这些关键参数后,利用次级人工神经网络的先进技术,将一个增强模型与主人工神经网络一起用于预测2023年事件的TEC值。研究结果表明,太阳参数显著提高了模型的准确性。值得注意的是,具有前导次网络的增强模型的恒星相关系数为0.99。从分布上看,41%的预测在(−1≤ΔTEC≤1)TECU谱内,28%在(1<ΔTEC≤2)和(- 2≤ΔTEC <−1)TECU范围,而相当多的30%跨越了更宽的(2<ΔTEC≤5)和(−5≤ΔTEC <−2)TECU范围。本质上,这项研究强调了结合太阳参数和先进的神经网络技术来改进电离层延迟预测的潜力,从而提高GNSS定位精度。
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Ionospheric TEC modeling using COSMIC-2 GNSS radio occultation and artificial neural networks over Egypt
Abstract The ionospheric delay significantly impacts GNSS positioning accuracy. To address this, an Artificial Neural Network (ANN) was developed using the high-quality COSMIC-2 ionospheric profile dataset to predict the Total Electron Content (TEC). ANNs are adept at addressing both linear and nonlinear challenges. For this research, eight distinct ANNs were cultivated. These ANNs were designed with the following inputs Year, Month, Day, Hour, Latitude, and Longitude. Along with solar and geomagnetic parameters such as the F10.7 solar radio flux index, the Sunspot Number (SSN), the Kp index, and the ap index. The goal was to discern the most influential parameters on ionosphere prediction. After pinpointing these key parameters, an enhanced model utilizing a pioneering technique of a secondary ANN was employed with the main ANN to predict TEC values for events in 2023. The study’s findings indicate that solar parameters markedly enhance the model’s accuracy. Notably, the augmented model featuring a prelude secondary network achieved a stellar correlation coefficient of 0.99. Distributionally, 41 % of predictions aligned within the (−1≤ ΔTEC ≤1) TECU spectrum, 28 % nestled within the (1< ΔTEC ≤2) and (−2≤ ΔTEC <−1) TECU ambit, while a substantial 30 % spanned the broader (2< ΔTEC ≤5) and (−5≤ ΔTEC <−2) TECU range. In essence, this research underscores the potential of incorporating solar parameters and advanced neural network techniques to refine ionospheric delay predictions, thus boosting GNSS positioning precision.
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来源期刊
Journal of Applied Geodesy
Journal of Applied Geodesy REMOTE SENSING-
CiteScore
2.30
自引率
7.10%
发文量
30
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