Improving the Computerized Ionospheric Tomography Performance Through a Neural Network-Based Initial IED Prediction Model

Hu, Tianyang; Xu, Xiaohua; Luo, Jia

doi:10.1109/tgrs.2023.3339166

Cited by 3 publications

(1 citation statement)

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“…C. Wang used the total electron content (TEC) obtained by the spaceborne full-polarization synthetic aperture radar (PolSAR) to correct the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), to improve the authenticity of the initial n e for MART [25]. With the develop of machine learning techniques, a neural network (NN) was applied by T. Hu to obtain highprecision initial ionospheric electron density for computerized ionospheric tomography (CIT), and better ionospheric electron density was reconstructed using IRI-2020 as the initial guess than using CIT [26]. All these methods of optimizing the initial electron density improve the accuracy of CIT, and it can be inferred that the evaluation and optimization of initial electron density is necessary for CIT.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Computerized Ionospheric Tomography over Maritime Areas Based on Simulated Slant Total Electron Content along Small-Satellite Constellation–Automatic Identification System Signal Rays

Li,

Xu,

Wang

et al. 2024

Atmosphere

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Ionospheres over sea areas have an inevitable impact on maritime–satellite communications; however, due to geographic constraints, ionospheric observation and analysis over sea areas are far from adequate. In our paper, slant total electron content (STEC) along small-satellite constellation–automatic identification system (AIS) signal rays is used for computerized ionospheric tomography (CIT) over sea areas, and small-satellite constellations can provide more effective signal rays than a single satellite. An adjustment factor δ is introduced to optimize the initial electron density for the multiplicative algebraic reconstruction technique (MART). The CIT results reconstructed by a traditional MART and our new method at 00:00 and 06:00, 15 March 2022, are compared, and our new method produces about a 15% and over 40% improvement in average deviation (AD) and root-mean-square error (RMSE). The results show that the bigger the difference between δ and 1, the better improvement will be in the 3D CIT process. The initial electron density is well selected during CIT when δ is approximate to 1, which is the case at 12:00, and the reconstructed 3D electron density, applying the initial ne and the adjusted initial ne, are both close to the true electron density. The small-satellite constellation–AIS signals are valuable resources for electron density reconstruction in sea areas.

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Section: Introductionmentioning

confidence: 99%