## 1 Introduction
There are two primary algorithms for atmospheric density inversion utilizing LEO satellites: The first algorithm involves indirectly calculating the mean atmospheric drag force acceleration using orbital data from LEO satellites, in conjunction with satellite at  
Figure 1: Search for articles using keywords “LEO Satellite” and “Low Earth Orbit Satellite” in the Web of Science database from 2015 to 2024 and keyword co-occurrence/clustering maps created using CiteSpace6.3.1 software in order to analyze research centered on LEO satellites and find the most important factors in the research of LEO satellites. In this figure, all words that co-occurred with LEO satellite keywords more than 50 times are displayed and automatically clustered according to the research field. titude and material properties as dictated by a dynamics model [14; 15]. This approach further facilitates the inversion of atmospheric density, allowing for comparison with traditional empirical models such as NRLMISISE-00 and DTM to validate the results. While this method is straightforward and computationally efficient, it faces challenges in practical satellite operations. Atmospheric density is closely linked to solar radiation, variations in the Earth's magnetic field, intrinsic atmospheric activity, and the Earth's rotation [16]. The unpredictability of changes in solar radiation and the Earth's magnetic field affects the drag coefficient experienced by satellites over time, and current predictive accuracy falls short of meeting the stringent requirements for high-precision space missions involving space rendezvous operations, collision warnings, and precision tracking. In response to this challenge, scientists have developed methods to directly measure the non-conservative absorption force on satellites using highly accurate onboard accelerometers [17]. Through the integration of data from multiple satellite accelerometers like CHAMP, GRACE, and SWARM, among others, the scientists improved the accuracy of calibration and determined the immediate acceleration of atmospheric drag forces on satellites during their operation for atmospheric density inversion [18; 19; 20]. The inaccuracies in accelerometer readings due to sensor hardware variances remain unresolved, necessitating further enhancements in data precision. Additionally, the frequency of data updates is relatively low, leading to considerable delays, and the consequent inversion error is also substantial. The global Low Earth Orbit satellites program is currently experiencing substantial growth, with an unprecedented number of satellite launches providing a robust dataset for scientific experimentation. However, there has been a marked decline in the availability of atmospheric density products derived from LEO satellite data. To evaluate the feasibility of utilizing LEO satellite data for atmospheric density inversion and to address challenges related to low precision in satellite orbit determination and significant delays in orbit prediction adjustments, we conducted a two-part study as follows. The first part encompasses research advancements regarding atmospheric density inversion based on Low Earth Orbit satellites. This includes methods for acquiring atmospheric density from these satellites, presented in Section 2, and progress made in atmospheric density inversion, discussed in Section 3. The second part involves an experimental investigation into atmospheric density inversion utilizing SWARM-A data. This example illustrates the viability of employing LEO satellite data for atmospheric density inversion while laying the groundwork for algorithm enhancements aimed at improving inversion accuracy and generating high-quality atmospheric density products based on extensive LEO satellite datasets. The acquisition of high-precision atmospheric density data are of paramount importance for the accurate prediction of satellite orbits. This can significantly reduce the impact of orbital decay on satellite life, thereby guaranteeing the safety of LEO satellite launches and operations.