# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 1 Introduction  
A precise estimate of the ionospheric electron density is required for a variety of ionospheric applications, including satellite navigation, space communications, and remote sensing systems. Due to technical observational constraints, ionosondes and high-power radars were primarily used in the early years to detect ionospheric electron density, resulting in the majority of electron density observations being concentrated at the bottom of the ionosphere in small areas and at low altitudes. A precise estimate of the ionospheric electron density is necessary for a variety of ionospheric applications, including satellite navigation, space communications, and remote sensing systems. The advancement of GPS technology has allowed detecting devices to acquire the global total electron content (TEC) property, which is the total of electron density per unit area from the bottom to the top of the ionosphere. The layered structure of the electron density is difficult to depict using TEC, and modelling based on TEC data is heavily reliant on information on three-dimensional space-time evolution (longitude, latitude, and time). As space observation technology advances, satellites carry more sophisticated observation equipment that can reverse recorded electrondensity profiles into electron density data comprising height information. Using electron density data, researchers have been able to model the three-dimensional electron density of the ionosphere. Many nations today conduct their own occultation experiments and have access to a wide range of space data products. The US Department of Defense and Taiwan together created the COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) program in 2006 to integrate data from various sources for ionospheric tomography. This project gave a new path to the creation of an ionosphere electron density model in four dimensions and to the realization of ionospheric tomography technology. The Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite 3 (COSMIC/FORMOSAT-3) is a joint US-Taiwan radio occultation mission consisting of six identical microsatellites. Each microsatellite carries a GPS occultation experiment (GOX) payload to operate the ionospheric radio occultation (RO) measurements [1]. The COSMIC satellites maintain an orbit of 72\({}^{\circ}\) inclination at an altitude of 800 km and provide 1000 to 2500 neutral atmosphere profiles and ionospheric electron density profile information per day. COSMIC has the characteristics of high vertical resolution, high precision, all-weather and long-term stability of global coverage, which can provide a large amount of valuable scientific data for ionospheric research. Computed tomography (CT) technology was mostly used in biology and medicine in the beginning, with remarkable success. Later, researchers used satellite radio signals and CT technologies to examine the space-time distribution of ionospheric electron density. In order to reflect the vertical structure of the ionosphere without using GPS, [PERSON] et al. originally developed ionospheric tomography (CIT) [2]. Ionospheric tomography was given a boost by [PERSON] et al. 's introduction of the multiplicative algebraic reconstruction technique (MART) [3]. The multiplicative algebraic reconstruction method has been used by certain researchers to perform ionospheric tomography and successfully recreate the ionospheric electron density [4; 5; 6; 7]. Tomography was used by [PERSON] et al. to create the electron density image of the Svalbard archipelago polar cap region [8]. After the establishment of the GNSS constellation, the GNSS-based ionospheric tomography technology made a breakthrough in reconstructing the four-dimensional structure of the ionosphere and overcoming the limitations of the three-dimensional electron density model. Based on GPS/MET occultation data and IGS observations, [PERSON] and others realised the real three-dimensional tomography using the Kalman filtering method [9].