Satellite pose estimation (PE) is crucial for space missions and orbital maneuvering. High-accuracy satellite PE could reduce risks, enhance safety, and help achieve the objectives of close proximity and docking operations for autonomous systems by reducing the need for manual control in the future. This article presents a joint iterative satellite PE and particle swarm optimization (PE-PSO) method. The PE-PSO method uses the number of batches derived from satellite PE as the number of particles and keeps the number of epochs from the satellite PE process as the number of epochs for PSO. The objective function of PSO is the training function of the implemented network. The output obtained from the previous objective function is applied to update the new positions of the particles, which serve as the inputs of the current training function. The PE-PSO method is tested on synthetic Soyuz satellite image datasets acquired from the Unreal Rendered Spacecrafts On-Orbit Datasets (URSOs) under different preset hyperparameters. The proposed method significantly reduces the incurred loss, especially during the batch-processing operation of each epoch. The results illustrate the accuracy improvement attained by the PE-PSO method over epoch processing, but its time consumption is not distinct from that of the conventional method. In addition, PE-PSO achieves better performance by reducing the mean position estimation error by 13.1% and the mean orientation estimation error on the testing dataset by 29.1% based on the pretrained weights of Common Objects in Context (COCO). Additionally, PE-PSO improves the accuracy of the Soyuz_hard-based weight by 7.8% and 0.3% in terms of the mean position estimation error and mean orientation estimation error, respectively.

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.

地球轨道的航天器越来越多, 近地空间密度不断增大. 据统计, 现有空间碎片主要来源于约640次的解体事件. 研究卫星解体事件对维持空间环境安全具有重要意义. 2021年11月15日, 俄罗斯进行了一次反卫星试验, 摧毁了一颗报废卫星—COSMOS 1408卫星. 事件产生了一片由约1800个可跟踪碎片组成的碎片云, 碎片云分布于200～1400 km高度区间, 随时间扩散演化, 对低地球轨道(Low Earth Orbit, LEO)卫星运行构成威胁. 本文基于美国空间监视网(Space Surveillance Network, SSN)发布的关于COSMOS 1408解体事件碎片的两行轨道根数(Two Line Element, TLE), 利用简化广义摄动模型第四版(Simplified General Perturbations 4, SGP4)模型对该碎片云进行演化分析, 主要包括碎片云的编目数量和时空变化、主要轨道参数变化、解体碎片对空间环境的影响, 并以事件碎片导致的国际空间站(International Space Station, ISS)四次机动规避碎片事件为例, 探究其演化规律以及带来的影响, 初步还原了COSMOS 1408反卫星事件碎片云的演化过程以及造成的影响.