For Geosynchronous Earth Orbit (GEO) objects, space-based optical surveillance has advantages over regional ground surveillance in terms of both the timeliness and space coverage. However, space-based optical surveillance may only collect sparse and short orbit arcs, and thus make the autonomous arc association and orbit determination a challenge for new GEO objects without a priori orbit information. In this paper, a three-step approach tackling these two critical problems is proposed. First, under the near-circular orbit assumption, a multi-point optimal initial orbit determination (IOD) method is developed to improve the IOD convergence rate and the accuracy of the IOD solution with angles-only observations over a short arc. Second, the Lambert equation is applied to associate two independent short arcs in an attempt to improve accuracy of the single-arc IOD semi-major axis (SMA) with the use of virtual ranges between the optical sensor and GEO object. The key idea in the second step is to generate accurate ranges at observation epochs, which, along with the real angle data, are then used to achieve much improved SMA accuracy. The third step is basically the repeated application of the second step to three or more arcs. The high success rate of arc associations and accurate orbit determination using the proposed approach are demonstrated with simulated space-based angle data over short arcs, each being only 3 min. The results show that the proposed approach is able to determine the orbit of a new GEO at a three-dimensional accuracy of about 15 km from about 10 arcs, each having a length of about 3 min, thus achieving reliable cataloguing of uncatalogued GEO objects. The IOD and two-arc association methods are also tested with the real ground-based observations for both GEO and LEO objects of near-circular orbits, further validating the effectiveness of the proposed methods.

This study aims to enhance Low Earth Orbit (LEO) satellite orbit prediction accuracy. We propose the Precise Orbit Determination with Optimized Perturbations (PODOP) method, considering Earth’s non-spherical gravity, atmospheric drag, etc., and a Long Short-Term Memory (LSTM)-based approach for orbital element time series. Validation shows that PODOP’s 10-day median error is 8.1 km (19% larger than Simplified General Perturbations (SGP4)’s 10.1 km) and LSTM’s 10-day median error is 5.3 km, outperforming SGP4 (48.5 km) and PODOP and improving constellation management and collision prevention.

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 Changchun Observatory of the National Astronomical Observatories, Chinese Academy of Sciences, and the Shanghai Astronomical Observatory are used to generate very short arc (VSA) angle observations of objects in low Earth orbit (LEO) and geostationary orbit (GEO) with their ground-based electrical–optical telescope arrays (EA), the Changchun EA and SAO FocusGEO, respectively. These observations are used in this paper. The range-searching (RS) algorithm for initial orbit determination (IOD) is improved through the multiple combinations of observations and the dynamic range-searching step length. Two different computation modes (the normal mode and the refining mode) of the IOD computation process are proposed. The geometrical method for the association is used. The IOD and association methods are extended to the real optical observations for both LEO and GEO objects. The results show that the IOD success rate of arcs from the LEO objects is about 91%, the error of the semimajor axis (SMA) of the initial orbital elements is less than 50 km, and the correlation accuracy rate is about 89%. The IOD success rate of arcs from the GEO objects is higher than 88%, and the correlation accuracy rate is greater than 87%. The recent COSMOS 1408 antisatellite test (ASAT) generated a large amount of debris. The algorithm of this paper and the observations of Changchun EA are used to initially identify new debris, possibly from the ASAT through initial orbit determination and track association. Finally, 64 suspected new pieces of debris can be found. The results show the effectiveness of the IOD and the correlation algorithm, as well as the potential application of the optical–electrical array in studying space events.

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.

Wide-area space surveillance sensors are the backbone to cataloging of Earth orbiting objects. Their core capability should be to efficiently detect as many space objects as possible over a large space domain. As such, the question of how to quantitively evaluate the object detection performance of the sensors is critical. The evaluation is traditionally performed by means of infield static tests and out-field calibration satellite tests. However, this simplified method is flawed in terms of its representativeness in spatial-temporal coverage and object types, because space objects vary greatly in orbit type, size, and shape, and thus the evaluation results may be overoptimistic. This paper proposes a practically implementable procedure to quickly and reliably evaluate the object detection performance of space surveillance sensors in which a catalog containing a vast number of on-orbit objects is used as a reference. It first constructs a unified model to estimate the size of objects from its radar cross section (RCS) data, then it presents a hierarchy scheme to efficiently compute object visibility, and finally, it makes the sensor performance evaluation through a data point matching technique. Experiments with two simulated sensors demonstrate that the realized performance is always inferior to the designed one, and in some cases the difference is significant and concerning. The presented approach could be routinely applied to evaluate the performance of any operational surveillance sensors and provide insight on how the sensor performance could be improved through refined design, manufacture, and operation.