Observation of a 27-day solar signature in noctilucent cloud altitude

Previous studies have identified solar 27-day signatures in several parameters in the Mesosphere/Lower thermosphere region, including temperature and Noctilucent cloud (NLC) occurrence frequency. In this study we report on a solar 27-day signature in NLC altitude with peak-to-peak variations of about 400?m. We use SCIAMACHY limb-scatter observations from 2002 to 2012 to detect NLCs. The superposed epoch analysis method is applied to extract solar 27-day signatures. A 27-day signature in NLC altitude can be identified in both hemispheres in the SCIAMACHY dataset, but the signature is more pronounced in the northern hemisphere. The solar signature in NLC altitude is found to be in phase with solar activity and temperature for latitudes $?70°$N. We provide a qualitative explanation for the positive correlation between solar activity and NLC altitude based on published model simulations.     

一种空间超大型可展开柔性聚光器

面向空间太阳能电站（SSPS）对大口径太阳能聚光器的应用需求,提出一种超大型、轻量化、高精度的空间可展开柔性聚光器的新构型。采取理论建模、计算仿真、优化设计相结合的方法对其抛物面构型原理、展开机构折展原理、展开态结构稳定性、动态聚焦精度等问题进行研究,并完成大口径可展开柔性聚光器的设计。提出了基于多层弹性花瓣镜体复合结构的大口径柔性聚光器优化创新设计方法。由于采用薄板柔索结构,本聚光器具有精度高、展收体积比大、质量轻、在线控制简单等优点,为空间太阳能电站千米级聚光器提供一种创新设计方法。     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

一种高光谱图像的双压缩感知模型

高光谱图像因其海量数据性,给存储、传输及后续分析处理带来了挑战。压缩感知理论提供了一种全新的信号采集框架。针对高光谱数据的三维特性,提出一种双压缩感知的采样与重构模型。该模型在采样阶段兼顾高光谱数据的空间和谱间稀疏特性,构造了能同时实现空间和谱间压缩采样的感知矩阵;重构阶段不同于传统的压缩感知重构方法直接重构高光谱数据,而是将高光谱数据分离成端元和丰度分别进行重构,然后利用重构的端元和丰度信息合成高光谱数据。实验结果表明,所提双压缩感知在低采样率下重构精度较三维压缩采样提高了10 dB以上,更为显著的是运算速度提升了3个数量级,同时该方法还便于获得端元和丰度信息。     

空间科学任务协同设计过程优化

为了对概念设计阶段空间科学任务协同设计过程进行合理规划, 减少设计反馈, 降低系统耦合度, 提出了设计结构矩阵(DSM)过程建模和遗传算法(GA) 过程优化算法. 该方法采用DSM对空间科学任务设计活动序列进行建模, 通过DSM描述设计活动间的信息依赖关系, DSM上三角之和代表该设计活动序列设计反馈次数; 将 DSM对应的设计活动序列视为染色体, 采用GA进行序列优化, 最小化设计反馈次数. 通过过程优化算法获取最佳设计活动序列, 优化设计过程, 降低系统耦合度. 空间科学任务实例分析结果表明, 该方法能够有效应用于空间科学任务协同设计的过程建模和过程优化, 指导设计过程的制定.      

日球层内外能量中性原子的探测

能量中性原子(Energetic Neutral Atoms, ENA, 简称能原子)是指在日球层内外空间, 拥有>0.1keV动能的原子.在此空间领域并没有温度>106K的中性气体, 但却充满动能>0.1keV的正离子.因此能原子A应该是A+离子与原地稀薄气体B原子或分子交换电荷所产生的, 即A++B→A+B+. 电荷交换涉及极小的动能变化, 新生的能原子A和离子B+基本上各自保持原有动能. 离子B+随即被当地磁场俘获, 能原子A则脱离磁场约束并携带其原属离子群的成分和能量信息而直线运动, 成为遥测空间等离子体的有效媒介. 美国人造卫星 IBEX (Interstellar Boundary Explorer) 直接探测得到来自日球层以外星际空间的能原子, 大幅延伸了利用能原子遥测空间等离子体的领域. 本文据此论述了空间能原子的发现, 综述了探测空间能原子的基本概念与实例、取得的主要成果、仪器设计和研制进展以及未来空间利用能原子遥测的发展趋势.      

对一种利用人造粉尘清除空间碎片新方法的理论分析

近地轨道的空间碎片污染日益严重,目前碎片数量已达到历史最高值并可能引发一系列连锁反应.进行主动碎片清除十分必要.利用粉尘主动清除近地轨道空间碎片是一种主动碎片清除的新方法.本文基于此方法的基本原理进行分析研究,建立了单颗碎片与人造粉尘作用的基本假设和机理模型,并对其作用进行定量计算分析;结合碎片的空间密度分布,对该方法的作用效果进行了定量估算,得出一些基本分析结论,有助于对新方法的客观认识.      

Application of Contact Graph Routing in Satellite Delay Tolerant Networks

Satellite networks have many inherent advantages over terrestrial networks and have become an important part of the global network infrastructure. Routing aimed at satellite networks has become a hot and challenging research topic. Satellite networks, which are special kind of Delay Tolerant Networks (DTN), can also adopt the routing solutions of DTN. Among the many routing proposals, Contact Graph Routing (CGR) is an excellent candidate, since it is designed particularly for use in highly deterministic space networks. The applicability of CGR in satellite networks is evaluated by utilizing the space oriented DTN gateway model based on OPNET(Optimized Network Engineering Tool). Link failures are solved with neighbor discovery mechanism and route recomputation. Earth observation scenario is used in the simulations to investigate CGR’s performance. The results show that the CGR performances are better in terms of effectively utilizing satellite networks resources to calculate continuous route path and alternative route can be successfully calculated under link failures by utilizing fault tolerance scheme.      

一种融合MRF分割与数学形态学的道路提取算法

针对高分辨率合成孔径雷达（Synthetic Aperture Radar, SAR）图像中道路目标难以有效提取的问题,提出一种融合马尔可夫随机场（Markov Random Field, MRF）分割与数学形态学处理的高分辨率SAR图像道路提取算法。该算法首先利用直方图均衡化和增强Lee滤波对SAR图像进行预处理,实现道路的边缘增强,抑制相干斑噪声;进而利用基于条件迭代模式（Iterated Conditional Mode, ICM）的MRF对SAR图像中的道路目标进行初分割;再用数学形态学填充空洞,平滑道路边缘;最后,基于道路的几何特征,使用偏心率、矩阵度、复杂度等因子去除虚警,从而提取出道路目标。利用该文算法对两块实际高分辨率SAR图像进行道路目标提取,均可以取得90%以上的正确道路提取率,表明本文算法具有较高的道路提取精度。     

一种校准空间蓄电池在不同加速度状态下电特性的装置CSCD

在飞行器发射升空之前,需对空间蓄电池进行一系列的测试。而地球上的环境和在太空中的环境存在很大的不同,尤其是太空的失重环境,在地球上很难长时间的模拟,以达到测试的目的。这样在地面上的测试数据可能与实际工作所得有所偏差。针对这种情况,文章提出了一种校准空间蓄电池在不同加速状态下电特性的装置。