Lei Liu

Accurate atmospheric drag modeling is essential for precise orbit determination and prediction of Low Earth Orbit satellites. A key component is the thermospheric density, typically estimated using empirical models driven by geomagnetic activity indices such as the 3‐hr Kp or ap. However, the coarse temporal resolution and fixed upper bounds of Kp/ap indices limit their ability to reflect rapid geomagnetic variations, especially during storms, leading to underestimated thermospheric density. In this study, we investigated the usefulness of replacing Kp/ap with the higher‐frequency and open‐ended Hpo/apo indices during geomagnetic storms. The correlation between Kp/ap and Hpo/apo indices over 126 geomagnetic storms was analyzed, and strong correlations (correlation coefficients > 0.93) were found. Using satellite‐derived density as a reference, we evaluated the performance of four empirical thermospheric models, including JB2008, NRLMSIS2.1, DTM2020 Operational, and DTM2020 Research, when driven by Hpo/apo. Results show that the performance of the JB2008 and DTM2020 Operational models is improved during moderate and intense geomagnetic storms but deteriorates under super storm conditions. The improvements are approximately 3%–9% for computed to observed (C/O) means and 4%–11% for C/O RMSEs with the JB2008 model, respectively, and less significant with the DTM2020 Operational model. For NRLMSIS2.1, only slight differences (<1% in means and <2% in RMSEs) were found in the model performance after index substitutions. Under super storm conditions, the impacts of index substitutions are statistically insignificant at the 95% confidence level. Additionally, increasing Hpo/apo temporal resolution from 60 to 30 min yields limited benefits (<1% improvement) for all models.

The increasing proliferation of space debris, intermittent space incidents, and the rapid emergence of massive LEO satellite constellations pose significant threats to satellites in orbit. Ground-based optical observations play a crucial role in space surveillance and space situational awareness (SSA). The Zhulong telescopic observation network stands as a pivotal resource in the realm of space object tracking and prediction. This publicly available network plays a critical role in furnishing essential data for accurately delineating and forecasting the orbit of space objects in Earth orbit. Comprising a sophisticated array of hardware components including precise telescopes, optical sensors, and image sensors, the Zhulong network synergistically collaborates to achieve unparalleled levels of precision in tracking and observing space objects. Central to the network’s efficacy is its ability to extract positional information, referred to as angular data, from consecutive images. These angular data serve as the cornerstone for precise orbit determination and prediction. In this study, the CPF (Consolidated Prediction Format) orbit serves as the reference standard against which the accuracy of the angular data is evaluated. The findings reveal that the angular data error of the Zhulong network remains consistently below 3 arcseconds, attesting to its remarkable precision. Moreover, through the accumulation of angular data over time, coupled with the utilization of numerical integration and least squares methods, the Zhulong network facilitates highly accurate orbit determination and prediction for space objects. These methodologies leverage the wealth of data collected by the network to extrapolate trajectories with unprecedented accuracy, offering invaluable insights into the behavior and movement of celestial bodies. The results presented herein underscore the immense potential of electric optic telescopes in the realm of space surveillance. By harnessing the capabilities of the Zhulong network, researchers and astronomers can gain deeper insights into the dynamics of space objects, thereby advancing our understanding of the cosmos. Ultimately, the Zhulong telescopic observation network emerges as a pioneering tool in the quest to unravel the mysteries of the universe.

The space surveillance network collects significant quantities of space object monitoring data on a daily basis, which varies in duration and contain observation errors. Cataloguing space objects based on these data may result in a large number of very short arcs (VSAs) being wasted due to cataloguing flaws, poor data quality, data precessing, and so on. To address this problem, an effective data mining method based on tracklet-to-object matching is proposed to improve the data utilization in new object cataloguing. The method can enhance orbital constraints based on useful track information in mined tracklets, improve the accuracy of catalogued orbits, and achieve the transformation of omitted observations into “treasures”. The performance of VSAs is evaluated in tracklet-to-object matching, which is less sensitive to tracklet duration and separation time than initial orbit determination (IOD) and track association. Further, the data mining method is applied to new space object cataloguing based on radar tracklets and achieved significant improvements. The 5-day data utilization increased by 9.5%, and the orbit determination and prediction accuracy increased by 11.1% and 23.6%, respectively, validating the effectiveness of our method in improving the accuracy of space object orbit cataloguing. The method shows promising potential for the space object cataloguing and relevant applications.

Concerns for the collision risk involving Starlink satellites have motivated the interest in obtaining their accurate orbit knowledge. However, accurate orbit determination (OD) and prediction (OP) of Starlink satellites confront two main challenges: mismatching or missed matching of sparse tracklets to maneuvering satellites, and unknown or unmodeled orbit maneuvers. How to exactly associate a tracklet to the right satellite is the primary issue, since a maneuvering satellite does not follow the naturally evolving orbit during the maneuvering, while more tracklets are needed for developing an accurate orbit maneuver model. If these two challenges are not well addressed, it may lead to catalog maintenance failure or even loss of objects. This paper proposes a method to correctly match tracklets to the climbing Starlink satellites. It is based on the recursive OD and OP, in which the orbit maneuver is modeled and the thrust is estimated, such that the subsequent OP accuracy guarantees the correct match of tracklets shortly after the OD time. Experiments with climbing Starlink satellites demonstrate that the tracklets within three days of the last TLE (two-line element) are all correctly matched to the right satellites. With the matched tracklets, the thrust accelerations of climbing Starlink satellites can be precisely estimated through an orbit control approach, and the position prediction accuracy over 48 hours is at the level of a few kilometers, providing accurate orbit knowledge for reliable collision warning involving Starlink satellites.

Thousands of orbit tracks of space objects are collected by a radar each day, and many may be from uncatalogued objects. As such, it is an urgent demand to catalogue the uncatalogued objects, which requires to determine whether two or more un-correlated tracks (UCTs) are from the same object. This paper proposes to apply the Lambert problem to associate two radar-measured orbit tracks of LEO and HEO objects. A novel method of position correction is proposed to correct the secular and short periodic effects caused by the J ₂ perturbation, making the Lambert problem applicable to perturbed orbit tracks. After that, an orbit selection method based on the characteristics of residuals solves the multiple-revolution Lambert problem. Extensive experiments with simulated radar measurements of LEO and HEO objects are carried out to assess the performance of the proposed method. It is shown that the semi-major axis can be determined with an error less than 200 m from two tracks separated by 4 days. The true positive (TP) rates for associating two LEO tracks apart by less than 6 days are 94.2%. The TP rate is still at 73.1% even for two tracks apart by 8–9 days. The results demonstrate the strong applicability of the proposed method to associate radar measurements of uncatalogued objects.