Estimation of dusk time F-region electron density vertical profiles using LSTM neural networks: A preliminary investigation

Lucas Alves Salles , Paulo Renato Pereira Silva , Guilherme Schwinn Fagundes , Jonas Sousasantos , Alison Moraes
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引用次数: 0

Abstract

The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System (GNSS). Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services. The objective of this study is to propose a neural network, trained with radio occultation data from the COSMIC-1 mission, that generates average ionospheric electron density profiles during dusk, focusing on the pre-reversal enhancement of the zonal electric field. Results show that the estimated profiles exhibit a clear seasonal pattern, and reproduce adequately the climatological behavior of the ionosphere, thus presenting strong appeal on ionospheric error attenuation.

利用 LSTM 神经网络估算黄昏时间 F 区域电子密度垂直剖面:初步研究
电离层密度的垂直剖面在低纬度赤道等离子体气泡(EPB)的发展中起着重要作用,反过来又会导致电离层闪烁,严重降低全球导航卫星系统(GNSS)关键用户的精度和可用性。通过垂直电子密度剖面准确估算电离层延迟对减少全球导航卫星系统误差和增强定位服务至关重要。本研究的目的是提出一种神经网络,利用 COSMIC-1 飞行任务的无线电掩星数据进行训练,生成黄昏期间电离层平均电子密度剖面图,重点是逆转前增强的地带电场。结果表明,估计的剖面图呈现出明显的季节性模式,并充分再现了电离层的气候学行为,因此对电离层误差衰减具有很强的吸引力。
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