Evaluation of Ionospheric Delay Extraction Model Using Dual-Frequency Multisystem Observations

Cheng, Jianhua; Jiang, Chao; Chun, Jung-Hwa; Ding, Jianguo; Li, Hui; Qi, Bing

doi:10.1109/jsen.2023.3278095

Cited by 1 publication

(1 citation statement)

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“…Moreover, the ionospheric delay has complex variations in spatial and temporal dimensionalities which range from several meters to several hundred meters. When the ranges of dimensionalities get increased, the error probability also gets increased and affects the models based on GNSS applications [6]- [9]. The largest naturally occurring error probability of GNSS is rectifiable with the help of broadcasting or empirical models of ionosphere which are generally utilized to diminish the ionospheric delay.…”

Section: Introductionmentioning

confidence: 99%

Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit

Basavarajaiah,

Garudachar

2024

Bulletin EEI

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An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.

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Section: Introductionmentioning

confidence: 99%