Regional TEC modelling over Africa using deep structured supervised neural network

Moses, Mefe; Dodo, Joseph D.; Ojigi, Lazarus Mustapha; Lawal, K. M.

doi:10.1016/j.geog.2020.05.004

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…As we can see, the ionospheric delays estimated from the DFPPP1, DFPPP2 and DFPPP3 models have the identical forms. The ionospheric observables can be viewed as the linear relationship of the STEC and SPR DCB [35]. To build the link of the STEC and VTEC, the ionospheric MF is usually established according to the satellite elevation.…”

Section: Ionospheric Modeling and Osb Estimationmentioning

confidence: 99%

Three Dual-Frequency Precise Point Positioning Models for the Ionospheric Modeling and Satellite Pseudorange Observable-Specific Signal Bias Estimation

Jin

2021

Remote Sensing

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Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) enables the estimation the ionospheric vertical total electron content (VTEC) as well as the by-product of the satellite Pseudorange observable-specific signal bias (OSB). The single-frequency PPP models, with the ionosphere-float and ionosphere-free approaches in ionospheric studies, have recently been discussed by the authors. However, the multi-frequency observations can improve the performances of the ionospheric research compared with the single-frequency approaches. This paper presents three dual-frequency PPP approaches using the BeiDou Navigation Satellite System (BDS) B1I/B3I observations to investigate ionospheric activities. Datasets collected from the globally distributed stations are used to evaluate the performance of the ionospheric modeling with the ionospheric single- and multi-layer mapping functions (MFs), respectively. The characteristics of the estimated ionospheric VTEC and BDS satellite pseudorange OSB are both analyzed. The results indicated that the three dual-frequency PPP models could all be applied to the ionospheric studies, among which the dual-frequency ionosphere-float PPP model exhibits the best performance. The three dual-frequency PPP models all possess the capacity for ionospheric applications in the GNSS community.

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Section: Ionospheric Modeling and Osb Estimationmentioning

confidence: 99%

Three Dual-Frequency Precise Point Positioning Models for the Ionospheric Modeling and Satellite Pseudorange Observable-Specific Signal Bias Estimation

Jin

2021

Remote Sensing

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“…Moses et al. (2020) constructed the African Regional Ionospheric TEC Model with deep learning technology to model TEC in the African region. They used spatiotemporal parameters (latitude, longitude, year, day of year, hour), auroral electrojet (AE) index, the disturbance storm time (Dst) index, and the F10.7 index as model inputs for predicting vertical total electron content (VTEC).…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al (2019Chen et al ( , 2023 further predict missing data of TEC map by a proposed deep learning algorithm: regularized deep convolutional generative adversarial network (RDCGAN), which suggests that deep learning can well extract the spatial feature of the TEC map. Moses et al (2020) constructed the African Regional Ionospheric TEC Model with deep learning technology to model TEC in the African region. They used spatiotemporal parameters (latitude, longitude, year, day of year, hour), auroral electrojet (AE) index, the disturbance storm time (Dst) index, and the F10.7 index as model inputs for predicting vertical total electron content (VTEC).…”

mentioning

confidence: 99%

Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion

Chen,

Wang,

et al. 2024

Space Weather

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Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models.

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“…Global Navigation Satellite Systems (GNSSs), including USA’s Global Positioning System (GPS), Russia’s GLObal NAvigation Satellite System (GLONASS), China’s BeiDou Navigation Satellite System (BDS) and Europe’s Galileo [ 1 ], have been widely used in various fields, such as navigation and timing, geodesy, seismic monitoring and gravity field [ 2 , 3 , 4 , 5 , 6 ]. However, GNSS positioning performance becomes seriously degraded in a challenging GNSS environment with few observing satellites and multipath effects.…”

Section: Introductionmentioning

confidence: 99%

Multi-GNSS Precise Point Positioning with UWB Tightly Coupled Integration

Huang

Jin

et al. 2022

Sensors

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Global Navigation Satellite Systems (GNSSs) can provide high-precision positioning services, which can be applied to fields including navigation and positioning, autonomous driving, unmanned aerial vehicles and so on. However, GNSS signals are easily disrupted in complex environments, which results in a positioning performance with a significantly inferior accuracy and lengthier convergence time, particularly for the single GNSS system. In this paper, multi-GNSS precise point positioning (PPP) with tightly integrating ultra-wide band (UWB) technology is presented to implement fast and precise navigation and positioning. The validity of the algorithm is evaluated by a set of GNSS and UWB data. The statistics indicate that multi-GNSS/UWB integration can significantly improve positioning performance in terms of the positioning accuracy and convergence time. The improvement of the positioning performance for the GNSS/UWB tightly coupled integration mainly concerns the north and east directions, and to a lesser extent, the vertical direction. Furthermore, the convergence performance of GNSS/UWB solution is analyzed by simulating GNSS signal interruption. The reliability and robustness of GNSS/UWB solution during GNSS signal interruption is verified. The results show that multi-GNSS/UWB solution can significantly improve the accuracy and convergence speed of PPP.

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Regional TEC modelling over Africa using deep structured supervised neural network

Cited by 13 publications

References 17 publications

Three Dual-Frequency Precise Point Positioning Models for the Ionospheric Modeling and Satellite Pseudorange Observable-Specific Signal Bias Estimation

Three Dual-Frequency Precise Point Positioning Models for the Ionospheric Modeling and Satellite Pseudorange Observable-Specific Signal Bias Estimation

Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion

Multi-GNSS Precise Point Positioning with UWB Tightly Coupled Integration

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