Target localization method of non-cooperative spacecraft on on-orbit service

With the explosion of the number of meteoroid/orbital debris in terrestrial space in recent years, the detection environment of spacecraft becomes more complex. This phenomenon causes most current detection methods based on machine learning intractable to break through the two difficulties of solving scale transformation problem of the targets in image and accelerating detection rate of high-resolution images. To overcome the two challenges, we propose a novel non-cooperative target detection method using the framework of deep convolutional neural network.Firstly, a specific spacecraft simulation dataset using over one thousand images to train and test our detection model is built. The deep separable convolution structure is applied and combined with the residual network module to improve the network’s backbone. To count the different shapes of the spacecrafts in the dataset, a particular prior-box generation method based on K-means cluster algorithm is designed for each detection head with different scales. Finally, a comprehensive loss function is presented considering category confidence, box parameters, as well as box confidence. The experimental results verify that the proposed method has strong robustness against varying degrees of luminance change, and can suppress the interference caused by Gaussian noise and background complexity. The mean accuracy precision of our proposed method reaches 93.28%, and the global loss value is 13.252. The comparative experiment results show that under the same epoch and batchsize, the speed of our method is compressed by about 20% in comparison of YOLOv3, the detection accuracy is increased by about 12%, and the size of the model is reduced by nearly 50%.     

基于多层次特征的高分影像海岸带地物分类

针对高分辨率影像地物光谱丰富复杂的问题，文章提出了一种结合像元-对象-空间格局特征的水域、盐田等海岸带精细地物分类方法。首先基于像元和空间格局特征分别利用遗传算法优化随机森林参数进行分类；其次将两种分类结果采用D-S证据理论融合；然后基于不同地物斑块的空间上下文信息，采用粒子群算法优化支持向量机（Support Vector Machine，SVM）参数实现面向对象的二次分类。为验证方法的有效性，收集山东省莱州湾海岸带附近区域的“高分二号”卫星影像，开展了水域及水利设施用地、盐田、工矿用地、交通运输用地、住宅用地、沿海滩涂、海域与耕地8类地物的分类实验，总体精度达到97.21%，Kappa系数为0.964 2。实验结果表明，文章提出的高分影像海岸带地物分类方法兼顾像元、对象及空间格局特征优点，能够实现多层次细致分类，并同时兼具较短的训练时长与较高的分类精度。该研究成果可为中国海岸带生态环境监测与土地利用规划提供一定技术支撑。     

Precise orbit determination and baseline consistency assessment for Swarm constellation

Precise orbit determination (POD) and precise baseline determination (PBD) of Swarm satellites with 4 years of data are investigated. Ambiguity resolution (AR) plays a crucial role in achieving the best orbit accuracy. Swarm POD and PBD based on single difference (SD) AR and traditional double difference (DD) AR methods are explored separately. Swarm antenna phase center variation (PCV) corrections are developed to further improve the orbit determination accuracy. The code multipath of C1C, C1W and C2W observations is first evaluated and clear variations in code noise related to different receiver settings are observed. Carrier phase residuals of different time periods and different loop tracking settings of receiver are studied to explore the effect of ionospheric scintillation on POD. The reduction of residuals in the polar and geomagnetic equator regions confirms the positive impact of the updated carrier tracking loops (TLs) on POD performance. The SD AR orbits and orbits with float ambiguity (FA) are compared with the Swarm precise science orbits (PSOs). An average improvement of 27 %, 4 % and 16 % is gained in along-track, cross-track and radial directions by fixing the ambiguity to integer. For Swarm-A/B and Swarm-B/C formations, specific days are selected to perform the DD AR-based POD during which the average distance of the formation satellites is less than 5000 km. Satellite laser ranging (SLR) observations are employed to validate the performance of FA, SD AR and DD AR orbits. The consistency between the SD AR orbits and SLR data is at a level of 10 mm which shows an improvement of 25 % when comparing with the FA results. An SLR residuals reduction of 15 % is also achieved by the DD AR solution for the selected days. Precise relative navigation is also an essential aspect for spacecraft formation flying missions. The closure error method is proposed to evaluate the baseline precision in three dimensions. A baseline precision of 1–3 mm for Swarm-A/C formation and 3–5 mm for Swarm-A/B and Swarm-B/C satellite pairs is verified by both the consistency check and closure error method.     

一种多目标变邻域模拟退火算法及成像星座任务规划方法

为提高成像星座观测任务的完成度和成像质量，提出一种多目标变邻域模拟退火算法(MOVNSA)。首先，分析了敏捷成像卫星观测任务约束和星上资源约束，构造了评价任务完成度和成像质量的目标函数，建立成像任务约束满足模型。然后通过设计多目标模拟退火算法中解的编码解码规则、变邻域搜索方法以及选拔淘汰机制，实现了观测任务成像时刻的滑动优化，兼顾提升了观测任务的完成度和成像质量。最后通过工程实例对多种算法进行测试和分析，结果表明该方法收敛速度快，优化效果好，可以在满足用户需求的前提下最大化任务的完成度。     

Assessing the performance of BDS-3 for multi-GNSS static and kinematic PPP-AR

As of 2021, a total of four different GNSS constellations – namely, GPS, GLONASS, Galileo, and BDS-3 – can be used with Full Operational Capability (FOC). In this work, the contribution of BDS-3 FOC to GPS + GLONASS + Galileo (GRE) PPP-AR is investigated, considering the three different cut-off angles (7°, 30°, and 45°) and different lengths of static observation sessions (24-, 12-, 6-, 3-, 1-, 0.5-, 0.25-hour). The data of 31 IGS-MGEX stations is processed with GRE PPP-AR and GREC3 (GPS + GLONASS (using float mode) + Galileo + BDS-3) PPP-AR modes. The results showed that BDS-3 degraded the horizontal (except for 24-h sessions) and vertical accuracy of static GRE PPP-AR solutions regardless of the elevation cutoff angle and observation time. The kinematic results showed that BDS-3 significantly contributed to the accuracy of GRE kinematic PPP-AR for 30° and 45° cutoff angles. The convergence time analysis showed that BDS-3 only contributes to GRE kinematic PPP-AR for the vertical component.     

天基海表精细探测微波载荷性能分析与展望

海表精细探测是海洋卫星未来发展的重要方向。介绍卫星海表参数探测的特点与优势，总结海表温度、海表高度、海表盐度等要素，分析天基探测载荷的发展现状。根据海表参数时空变化特性，分析高时空分辨率微波探测载荷性能需求。在此基础上，针对美国和欧洲相关卫星系统发展计划，分析了高时空分辨率、高精度天基微波探测载荷可实现性与未来发展趋势，针对海表精细探测天基微波载荷的发展提出了建议。     

大气环境监测卫星紫外高光谱大气成分探测仪技术发展及应用

大气环境监测卫星是我国民用空间基础设施中长期发展规划中的科研卫星。本文介绍了搭载于该卫星的新一代紫外高光谱大气成分探测仪，通过发展紫外高光谱大幅宽高分辨超光谱成像技术，使紫外谱段高光谱大气观测空间分辨率提升一倍，大幅提升气态污染物监测能力，取得良好的应用效果。     

一种改进的补偿因子区域不变CS算法及Simulink模型建模

相位补偿因子、距离向补偿因子和方位向补偿因子的产生是合成孔径雷达实时CS（Chirp Scaling，调频变标）算法中的关键。针对一种现有的补偿因子区域不变CS算法进行了改进，对于更新步长内各单元的统一补偿因子，采用该区域内所有单元频率平均值对应的补偿因子，代替原来使用的第一个单元对应的补偿因子，使得最终的补偿结果相比于原有算法更加均匀，计算量与原有算法相当。经过Matlab仿真验证，改进算法的成像指标得到提升。为便于生成FPGA代码，采用Simulink工具搭建了用于生成改进算法中三种补偿因子的模型。将Simulink模型输出的代码加载到Vivado软件中，对Vivado输出的补偿因子与由Matlab输出的精确补偿因子进行了对比，精度满足要求，验证了所搭建Simulink模型的准确性。     

大气环境监测卫星激光雷达技术

星载激光雷达是高精度测量全球大气CO2柱浓度和气溶胶垂直廓线的重要手段，大气环境监测卫星大气探测激光雷达(ACDL)在一套激光雷达中集成了2种探测体制，采用1 572 nm积分路径激光差分吸收方法测量全球CO2柱浓度，采用532 nm高光谱探测(HSRL)方法测量气溶胶和云的垂直廓线，均为国际首次在星载平台成功验证，是国际首个星载CO2探测激光雷达和国际首个高光谱气溶胶探测激光雷达。在国际已发射的星载激光雷达中，ACDL激光雷达是探测体制最全、波长最多、能量最高、频率最稳和精度最准的激光雷达，实现了创新跨越。大气探测激光雷达在轨连续工作，首次获得了全天时精度优于1 ppm的全球CO2柱浓度、精度优于20%的气溶胶廓线等遥感数据。本文介绍了ACDL激光雷达的工作原理、系统参数以及相关的定标测试结果。     

多波束三维成像激光雷达高精度收发匹配方法研究

三维成像激光雷达技术为空间地理信息获取、目标立体探测提供了新的技术途径。以多波束并行收发、单光子阵列探测为特征的新一代三维成像激光雷达可提升雷达探测灵敏度、激光成像帧频与成像效率，从而实现远距离快速激光三维成像。三维成像激光雷达的激光发散角与接收视场需角秒量级的匹配精度。随着系统波束数量的增加，几十甚至上百波束的高精度收发波束匹配是系统设计装调的难点。聚焦多波束单光子阵列三维成像激光雷达的收发匹配难题，设计了基于衍射分束激光发射与光纤阵列接收的收发光学系统，提出了一种多波束激光雷达收发波束匹配方法，并对该方法的收发匹配误差源以及温度对其影响进行了详细分析仿真。实验验证结果表明，可实现64波束优于10 μrad的匹配误差，在工作温度范围内总的收发失配优于20.78 μrad，满足本系统设计时25.5 μrad的收发匹配裕量，系统对环境温度有较好的适应性，具有良好的工程应用前景。