Atmospheric Density Model Optimization and Spacecraft Orbit Prediction Improvements Based on Q-Sat Orbit Data

Atmospheric drag calculation error greatly reduce the low-earth orbit spacecraft trajectory prediction fidelity. To solve the issue, the "correction - prediction" strategy is usually employed. In the method, one parameter is fixed and other parameters are revised by inverting spacecraft orbit data. However, based on a single spacecraft data, the strategy usually performs poorly as parameters in drag force calculation are coupled with each other, which result in convoluted errors. A gravity field recovery and atmospheric density detection satellite, Q-Sat, developed by xxxxx Lab at xxx University, is launched on August 6th, 2020. The satellite is designed to be spherical for a constant drag coefficient regardless of its attitude. An orbit prediction method for low-earth orbit spacecraft with employment of Q-Sat data is proposed in present paper for decoupling atmospheric density and drag coefficient identification process. For the first step, by using a dynamic approach-based inversion, several empirical atmospheric density models are revised based on Q-Sat orbit data. Depends on the performs, one of the revised atmospheric density model would be selected for the next step in which the same inversion is employed for drag coefficient identification for a low-earth orbit operating spacecraft whose orbit needs to be predicted. Finally, orbit forecast is conducted by extrapolation with the dynamic parameters in the previous steps. Tests are carried out for the proposed method by using a GOCE satellite 15-day continuous orbit data. Compared with legacy “correction - prediction” method in which only GOCE data is employed, the accuracy of the 24-hour orbit prediction is improved by about 171m the highest for the proposed method. 14-day averaged 24-hour prediction precision is elevated by approximately 70m.

System modeling in data processing of Taiji-1 mission

Taiji-1 is the first technology demonstration satellite of the Taiji Program in Space, which, served as the pre-PathFinder mission, had finished its nominal science operational phase and successfully accomplished the mission goal. The gravitational reference sensor (GRS) on-board Taiji-1 is one of the key science payloads that coupled strongly to other instruments, sub-systems and also the satellite platform itself. Fluctuations of the physical environment inside the satellite and mechanical disturbances of the platform generate important noises in the GRS measurements, therefore their science data can also be used to evaluate the performance of the [Formula: see text]-thrusters and the stability of the platform. In this work, we report on the methods employed in Taiji-1 GRS data processing in the systematical modelings of the spacecraft orbit and attitude perturbations, mechanical disturbances, and internal environment changes. The modeled noises are then removed carefully from the GRS science data to improve the data quality and the GRS in-orbit performance estimations.

Solar Orbiter observations of solar wind current sheets and their deHoffman-Teller frames

<p>Solar wind current sheets have been extensively studied at 1 AU. The recent advent of Parker Solar Probe and Solar Orbiter (SolO) has enabled us to study these structures at a range of heliocentric distances.</p><p>We present SolO observations of current sheets in the solar wind at heliocentric distances between 0.55 and 0.85 AU, some of which show signatures of ongoing magnetic reconnection. We develop a method to find the deHoffman-Teller frame which minimizes the Y-component (the component tangential to the spacecraft orbit) of the electric field. Using the electric field measurements from RPW and magnetic field measurements from MAG, we use our method to determine the deHoffman-Teller frame of solar wind current sheets. The same method can also be used on the Alfvénic turbulence and structures found in the solar wind to obtain a measure of the solar wind velocity.</p><p>Our preliminary results show a good agreement between our modified deHoffmann-Teller analysis based on the single component E-field, and the conventional deHoffman-Teller analysis based on 3D plasma velocity measurements from PAS. This opens up the possibility to use the RPW and MAG data to obtain an estimate of the solar wind velocity when particle data is unavailable.</p>