空间拦截仿真平台设计

仿真平台研制是系统仿真的重要课题.根据空间拦截过程仿真的需要,提出了一个可集成多种空间拦截任务、便于扩展、具有完善可视化功能的仿真平台设计方案.介绍了仿真平台的层次划分、各层次的主要功能以及层次间的关系.详细阐述了仿真平台的整体框架.重点研究了平台可视化的实现技术和集成多种拦截任务的具体方法.实际应用表明:该平台可以完成多种空间拦截任务的仿真并具备足够的扩展能力,完备的可视化功能更为空间拦截问题的研究提供了便利.      

天文观测卫星动中探测机动测试系统设计

天文观测卫星普遍采用动中探测模式开展空间探测任务。为了实现动中探测姿态机动模式的地面有效验证，设计了整星机动集成测试方案。设计惯性空间基准的地面动力学仿真，满足惯性空间扫描仿真；设计整星各分系统间高精度时间统一系统，为整星机动测试提供统一基准；基于STK与Matlab接口模块，设计基于实时遥测数据的可视化判读系统，可提高测试判读实时性与准确度。整星测试结果表明，该方案有效验证了动中探测任务中姿态机动功能的正确性和指标符合情况，为开展整星地面测试和在轨应用提供了参考。     

空间天气学国家重点实验室

<正>空间天气学国家重点实验室依托中国科学院国家空间科学中心,是中国空间科学领域唯一的国家重点实验室,开辟了中国空间天气探测和研究的新方向。实验室前身是1999年成立的中科院空间天气学院重点实验室,2009年成为国家重点实验室。现任实验室主任为王赤研究员,学术委员会主任为万卫星院士。1.总体定位面向空间科学国际前沿和国家对空间天气应用服务的需求,开展日地空间和太阳系空间天气变化过程监测研究。     

空间机械臂地面仿真与测试系统设计

摘要： 根据某型号搭载的七自由度空间机械臂的测试任务,设计一套空间机械臂地面仿真与测试系统.该系统可以完成两方面的功能：一是利用空间机械臂模拟器进行半物理仿真,对空间机械臂控制线路盒的电接口和控制软件功能进行测试;二是采用吊丝卸载装置对空间机械臂真实产品进行全物理试验,对在轨任务进行地面演示验证.利用所设计的测试系统已经完成了某型号空间机械臂的地面测试与演示验证任务,目前该型号已经发射成功,空间机械臂已经成功完成在轨试验.所设计的空间机械臂地面仿真与测试系统具有较好的通用性和扩展性,可以应用于其他空间机械臂产品的地面测试.     

全球空间天气路线图及对中国的启示

随着科技的发展,空间天气对电力系统、通信导航系统和航天资产等遍布全球的技术基础设施的影响越来越深.需要加强对空间天气事件过程的理解,提升空间天气的预报能力,优化基础设施设计,从而减缓空间天气对社会造成的影响.基于这些需求,国际空间研究委员会(COSPAR)联合国际与日共存计划(ILWS)共同成立专家组,研究制定了全球2015-2025空间天气发展路线图.本文对该路线图进行介绍和解读,讨论该路线图对中国空间天气发展的启示.     

中国空间地球科学发展现状及未来策略

空间地球科学是以空间对地观测为主要信息获取手段,研究地球系统的各圈层及之间的相互作用、过程与演变,对地球进行系统研究的综合性交叉学科.为纪念空间科学学会成立40周年,本文系统回顾了中国空间地球科学的发展历程,分析当前面临的机遇与挑战,进而提出中国空间地球科学未来发展的建议.     

利用Hp分量预测空间环境地磁活动指数Kp

地磁场扰动可以引起近地空间环境(包括电离层和磁层)一系列变化,地磁Kp指数是空间天气扰动的重要参考指标.采用地球同步轨道GOES-8卫星监测到的垂直于同步卫星轨道平面的地磁分量Hp数据,分析了地磁Kp指数与Hp分量波动幅度间的统计关系,结果显示,Hp分量的变化与Kp指数具有很好的相关性.利用回归分析和RBF神经网络方法,建立了Kp指数现报模型,根据地球同步轨道地磁场Hp分量的变化,计算出相同时段的Kp指数.监测结果表明,预报方法具有一定的有效性和实用性,特别是人工神经网络模式计算的Kp指数与实测结果吻合很好.利用此方法能够在不依赖于地面地磁探测数据的情况下,快速预报地磁扰动,及时为空间天气保障提供参考.同时,鉴于中国即将发射的风云四号搭载有地磁场探测仪,本项研究可为自主数据的应用奠定基础.     

太阳与内日球立体探测卫星计划科学目标建议

面向认知太阳风暴和保障航天安全的国家战略需求，中国近年来提出太阳与内日球立体探测卫星计划，拟适时部署环黄道面的拉格朗日点L3-L4-L5定点卫星星座和脱离黄道面的太阳极轨对偶卫星。通过该卫星计划的初步科学论证分析，建议将其科学目标凝练为太阳磁场、太阳风暴、太阳风，将其应用目标定位于日地空间天气预报，建议配置成像类、粒子类、波场类的综合科学载荷。此外，利用日地空间的三维磁流体数值模拟和计算机图形学的艺术可视化，形象地表达环黄道面卫星“对日凝视”和太阳极轨卫星“鸟瞰全景”的探测概念。中国太阳与内日球立体探测卫星计划将揭示太阳磁场的起源规律及其爆发机理、日地耦合系统的空间天气变化机理，并能够为日地空间天气三维数值建模提供观测数据驱动的初边值条件，必将极大推动空间天气的立体监测、前沿研究、精准预报的一体化进程。     

欧洲空间科学委员会发布空间天气一体化研究报告

欧洲空间科学委员会(European Space Science Committee,ESSC)2019年8月在Journal of Space Weather and Space Climate发表关于欧洲空间天气一体化方法的评估报告。报告分析了欧洲当下应对空间天气行动存在的问题,并相应提出了协调和优化整合建议,旨在增强欧洲应对空间天气风险的能力,提升欧洲对全球行动的贡献。ESSC对欧洲应对空间天气行动的现状从六个方面进行了深入分析。(1)在空间天气的科学认知方面,亟需加强对多个空间天气耦合机制的科学理解,改善对日地耦合系统的总体描述。     

基于集成学习的太阳质子事件短期预报方法

太阳质子事件是一种由太阳活动爆发时喷射并传播到近地空间的高能粒子引起的空间天气现象。这些高能粒子会对航天器和宇航员产生严重危害,对太阳质子事件进行准确的短期预报是航天活动灾害预防的重要内容。针对当前主要预报模型中普遍存在的高虚报率问题,提出了一种基于集成学习的太阳质子事件短期预报方法,利用第23个太阳活动周数据,建立了一种集成8种机器学习模型的太阳质子事件短期预报系统。实验结果表明,本文方法在取得了80.95%的报准率的同时,将虚报率降低至19.05%,相比现有的预报系统具有较为明显的优势。      

Progress in space weather predictions and applications

The methods of today’s predictions of space weather and effects are so much more advanced and yesterday’s statistical methods are now replaced by integrated knowledge-based neuro-computing models and MHD methods. Within the ESA Space Weather Programme Study a real-time forecast service has been developed for space weather and effects. This prototype is now being implemented for specific users. Today’s applications are not only so many more but also so much more advanced and user-oriented. A scientist needs real-time predictions of a global index as input for an MHD model calculating the radiation dose for EVAs. A power company system operator needs a prediction of the local value of a geomagnetically induced current. A science tourist needs to know whether or not aurora will occur. Soon we might even be able to predict the tropospheric climate changes and weather caused by the space weather.     

Space weather center in Japan

Progress in information technology has enabled to collecting data in near real-time. This significantly improves our ability to monitor space weather conditions. We deliver information on near real-time space weather conditions via the internet. We have started two collaborations with space weather users. One is a measurement of geomagnetically induced current (GIC) of power grids in collaboration with a Japanese power company. The other concerns radiation hazards for aircrews. The radiation exposure level for aircrews was been determined by the Japanese government by the end of 2005. The proposed upper limit is 5 mSV a year. We are actively seeking ways to contribute to this subject. Our activities at the Japanese space weather center are reported in this paper.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

敏捷卫星控制分系统动中成像姿态机动模式的集成测试方案设计

摘要： 动中成像姿态机动模式作为敏捷卫星一种新的工作模式,具有机动过程复杂、姿态实时变化、指标要求更高的特点,原有的集成测试环境和测试方法难以对其进行有效验证,针对这一问题,设计了分系统集成测试方案.该方案对分系统集成测试环境的主控机、动力学模型和数据分析系统进行设计,实现了基于任务的自主测试、目标姿态计算模型和可视化测试功能,对测试方法中的机动模式的典型测试工况和指标测试进行了研究.测试结果表明,该方案可有效验证控制分系统姿态机动功能的正确性和指标符合情况,为开展分系统集成测试提供了思路.     

新的适于计算机辅助工程的算法集成模型

为解决计算机辅助工程中"以算法为核心"类软件系统在算法集成方面存在的重要问题,以火箭发动机设计这个较具代表性的特例为切入点,深入分析工程实践中算法的运用特点,并指出现有软件系统的不足.提出2种算法模式,基于这2种算法模式建立一种新的算法集成模型--轮循与层次化算法集成模型,其具有松耦合、层次化、灵活等特点,基于该模型可建立一个跨领域、通用、高效的算法集成软件环境.     

Space weather in Japan

In Japan, Communications Research Laboratory engages in operational space environment information services as National Forecasting Center and Regional Warning Center of ISES. Data from local observations and data collected via internet from domestic and foreign institutes are used for the daily operational forecast. Fundamental research on space weather issues has been carried out at several institutes and universities, including STE Laboratory and NASDA. In this presentation, an overview of current space weather forecast operations and a system for information outreach in Japan will be presented. Current and future observation programs from ground-base and space will be also briefly reviewed.     

高分辨率多模式星载SAR系统设计方法

针对当前星载合成孔径雷达(SAR,Synthetic Aperture Radar)朝高分辨率、多模式方向快速发展的趋势,兼顾星载SAR系统多种工作模式的特点,总结星载SAR系统的星地一体化指标体系;在此基础上提出了高分辨率、多模式星载SAR系统星地一体化设计方法,给出了系统星地一体化设计流程,对雷达天线参数设计、雷达系统参数设计、雷达发射功率选择进行重点分析;以国外高分辨率、多模式星载SAR系统为例,以其总体技术指标及轨道参数为依据,设计了系统的中央电子设备参数、天线参数及波位参数,并分析了系统的性能,从而对所提出的设计方法进行了验证.该方法克服了现有方法的局限性,为高分辨率、多模式星载SAR系统研制提供了技术支撑.     

基于空间卫星综合应用系统的天气预报业务框架构建

围绕空间卫星综合应用系统，在介绍天气预报业务系统基本框架的基础上，着重分析了天气预报业务化框架、网络的技术特点，以及天气预报业务的一些技术和方法．     

Space Weather Service for Chinese Space Science Satellites

Strategic Priority Research Program on Space Science has gained remarkable achievements. Space Environment Prediction Center (SEPC) affiliated with the National Space Science Center (NSSC) has been providing space weather services and helps secure space missions. Presently, SEPC is capable to offer a variety of space weather services covering many phases of space science missions including planning, design, launch, and orbital operation. The service packages consist of space weather forecasts, warnings, and effect analysis that can be utilized to avoid potential space weather hazard or reduce the damage caused by space storms, space radiation exposure for example. Extensive solar storms that occurred over Chinese Ghost Festival (CGF) in September 2017 led to a large enhancement of the solar energetic particle flux at 1 AU, which affected the near Earth radiation environment and brought great threat to orbiting satellites. Based on the space weather service by SEPC, satellite ground support groups collaborating with the space Tracking, Telemetering and Command system (TT&C) team were able to take immediate measures to react to the CGF solar storm event.