The accuracy of atmospheric mass density models is critical for OP (orbit prediction) of LEO (low earth orbit) space objects. This paper studies the effects of three methods on the improvement of OP accuracy for LEO objects, where only TLE (two-line element) data are used. Fifty objects at altitudes between 400 and 600 km are chosen for the study, with 20 or 40 being used as calibration objects, and the others as verification objects. The results show that about a 30 percent reduction in OP errors can be achieved for a 10-day prediction time.

Acquiring accurate space object orbits is crucial for many applications such as satellite tracking, space debris detection, and collision avoidance. The widely used two-line element (TLE) method estimates the position and velocity of objects in space, but its accuracy can be limited by various factors. A combination of multiple TLEs and advanced modeling techniques such as batch least squares differential correction and high-precision numerical propagators can significantly improve TLE accuracy and reliability, ensuring better space object surveillance. Previous studies analyzed additional factors that may influence TLE accuracy and evaluated the accuracy of Starlink TLE using precise ephemeris data from SpaceX. The results indicate that utilizing multiple TLEs for precise orbit determination can significantly enhance the performance of orbit prediction methods, particularly when compared to SGP4. By leveraging 10-day Starlink TLEs, the accuracy of 5-day predictions can be improved by approximately twofold. Additionally, producing two pseudo-observations within an orbital period near the TLE epoch yields the greatest effect on prediction accuracy, with this distribution of pseudo-observations increasing accuracy by approximately 10% compared to a uniform distribution. Further research can explore more data fusion and machine learning approaches to optimize operations in space.

The accuracy of the atmospheric mass density is one of the most important factors affecting the orbital precision of spacecraft at low Earth orbits (LEO). Although there are a number of empirical density models available to use in the orbit determination and prediction of LEO spacecraft, all of them suffer from errors of various degrees. A practical way to reduce the error of a particular model is to calibrate the model using precise density data or tracking data. In this paper, a long short-term memory (LSTM) neural network is proposed to calibrate the NRLMSISE-00 density model, in which the densities derived from spaceborne accelerometer data are the main input. The resulted LSTM-NRL model, calibrated using the accelerometer data from Challenging Minisatellite Payload (CHAMP) satellite, is extensively experimented to evaluate the calibration performance. With data in one month to train the LSTM-NRL model, the model is shown to effectively reduce the root mean square error of the model densities outside the training window by more than 40% in various time spans and space weather environment. The LSTM-NRL model is also shown to have remarkable transferring performance when it is applied along the GRACE satellite orbits.

The space environment is becoming increasingly crowded, raising the likelihood of collisions between satellites. Accurate prediction of satellite orbits is crucial for space transportation and communications. This paper proposes an orbit prediction method based on the long short-term memory (LSTM) neural network algorithm and two-line elements (TLE). The effectiveness of the proposed method was validated and evaluated by selecting space objects from different orbits (LEO, MEO, GEO). Six months of TLE data for these space objects were collected. The predicted orbits for LEO using the LSTM and SGP4 methods were compared with reference orbits derived from precision orbits released by the International Laser Ranging Service (ILRS). Calculations were performed every two days for six months of data, and the results indicate that LSTM can improve the orbit prediction accuracy of these satellites by at least 20% over half a month.

Accurately modeling the density of atmospheric mass is critical for orbit determination and prediction of space objects. Existing atmospheric mass density models (ADMs) have an accuracy of about 15%. Developing high-precision ADMs is a long-term goal that requires a better understanding of atmospheric density characteristics, more accurate modeling methods, and improved spatiotemporal data. This study proposes a method for calibrating ADMs using sparse angular data of space objects in low-Earth orbit over a certain period of time. Applying the corrected ADM not only improves the accuracy of orbit determination, but also enhances the accuracy of orbit prediction beyond the correction period. The study compares the impact of two calibration methods: atmospheric mass density model coefficient (ADMC) calibration and high precision satellite drag model (HASDM) calibration on the accuracy of orbit prediction of space objects. One month of ground-based telescope array angular data is used to validate the results. Space objects are classified as calibration objects, participating in ADM calibration, and verification objects, inside and outside the calibration orbit region, respectively. The results show that applying the calibrated ADM can significantly increase the accuracy of orbit prediction. For objects within the calibration orbit region, the calibration object’s orbit prediction error was reduced by about 55%, while that of verification objects was reduced by about 45%. The reduction in orbit prediction error outside this region was about 30%. This proposed method contributes significantly to the development of more reliable ADMs for orbit prediction of space objects with sparse angular data and can provide significant academic value in the field of space situational awareness.