空间科学任务及其特点综述

人类进入太空以来已经发射了近1万个人造航天器,其中大约10%执行的是空间科学和探测任务.近年来中国经济快速发展,提供了更多的基础研究经费,经济转型也对创新驱动发展提出了更高需求,中国对空间科学的投入开始逐年增加.2015年以来先后成功发射了悟空号、实践十号、墨子号和慧眼号4颗科学卫星,天宫2号空间实验室也成功实施了一系列空间科学实验.重要科学发现和成果正在不断地产出.空间科学卫星任务（或称计划）与应用卫星从提出到评价都有很大不同,因此有必要对其所具有的特点进行分析,从而引导空间科学界从科学团队、技术团队到管理团队提高认识,确保未来的空间科学任务发挥最大效益,获得最大科学产出.     

一种地震动信号特征提取与分类方法

为了实时检测、识别和预警对地下基础设施的挖掘破坏活动,本文提出一种地震动信号特征提取与分类方法。通过提取小波包变换域和集合经验模态变换域的多域能量联合分布特征向量,构建改进的径向基神经网络分类模型,利用机器学习的方法提取稳定的信号多域融合特征,并实现准确的信号特征分类预测。由多类别挖掘信号的仿真实验结果可以看出,本文的算法和模型能有效提升地震动信号分类的准确率,对地震动干扰信号具有较强的鲁棒性。     

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional（2-D）histogram region oblique division and artificial bee colony（ABC）optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional（1-D）method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization（NCPSO）,the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.     

无人机编队支撑网络的协同通信中继策略

针对无人机编队支撑网络优化选择中继成员进而提高编队协同通信能力问题，基于端到端技术（Device-to-Device，D2D），提出了一种同时考虑无人机编队成员的邻近域及编队成员间的社会性依存关系来优化选择中继成员的中继策略，旨在既能保证无人机编队的基本通信能力，又能同时优化提高编队的吞吐量，从而进一步提升编队支撑网络的整体性能。研究结果表明，所提的无人机编队支撑网络中继成员优化选择策略在系统吞吐量方面较已有方法具有明显优势。     

Material selection for a CubeSat structural bus complying with debris mitigation

The increasing number of commercial, technological and scientific missions for CubeSats poses several concerns about the topic of space junk and debris mitigation. As no regulation is currently in place, innovative solutions are needed to mitigate the impact that Low Earth Orbit objects can have during uncontrolled re-entry and the associated potential events of surface collision. We investigated the requirements, in terms of materials selection, for the development of a 3D-printed structural bus able to withstand loads during launch and in-orbit operations, with the objectives to be as light as possible and requiring the least amount of heat for demise during atmospheric re-entry. The selection indicated magnesium alloys as the best candidates to improve the reference material, aluminium 6061 T6, resulting in both mass-reduction and improved demisability. We also analysed how the relative importance of these two objectives can modify the selection of materials: if minimizing the heat to disintegration were valued more highly than lightness, for example, the new best candidates would become tin alloys. Our analysis, furthermore, suggested the importance of Liquid Crystal Polymer as the sole plastic material approaching the performance of the best metal choices. This contribution, thus, provides novel insight in the field of 3D-printed materials for the fast-growing CubeSat segment, complying with the debris mitigation initiatives promoted by space agencies and institutions.     

基于海杂波稀疏性与非均匀度的样本挑选方法

针对预警雷达对海监视面临海杂波分布非均匀与杂波样本受目标污染,导致自适应杂波抑制处理性能恶化和目标能量损失的问题,提出了一种基于海杂波稀疏性与非均匀度的样本挑选方法。该方法将目标的导向约束与广义内积样本挑选方法结合,先利用海杂波在空时二维平面上的稀疏分布特性,根据海杂波与目标空时二维分布差异剔除被目标污染的样本,再利用广义内积准则衡量海杂波分布的非均匀程度,并获取均匀样本,以提高杂波协方差矩阵的估计精度。仿真结果表明:所提方法能在提高杂波抑制性能的同时,减小目标信号能量损失。该方法可广泛应用于海面预警监视雷达系统。     

深空自主光学导航小行星筛选与规划方法研究

 深空探测器的自主光学导航是深空探测器自主的关键技术之一,而导航路标——小行星的选择与拍照序列的规划是深空探测自主导航的重要内容。基于探测Ivar小行星的设计方案,在导航小行星筛选方面,提出了基于小行星可见星等、距离、相对速度以及视线夹角的筛选准则,并利用综合评估的方式进行导航小行星的筛选;拍照序列规划方面,提出差额筛选的策略优化导航性价比,并利用改进的遗传算法进行规划。数学仿真结果表明,该方法能够有效地进行小行星的选择与拍照序列的规划。     

Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause’s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples, the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.     

一种多分辨率图像目标检测算法

为解决航天遥感图像分辨率和目标尺度变化大的挑战,提出了一种基于多分辨率图像的目标检测算法。改进了自适应特征金字塔和轻量级的分类预测模块,通过使用注意力机制,从不同层次的特征图中提取语义信息。引入了一种预测目标尺度的方法,以分析图像中目标的分布和尺度信息。将算法在DOTA（Dataset for Object deTection in Aerial Images）数据集上进行了实验验证,在U-Net（一种基于卷积神经网络的语义分割算法）和ResNet-34（一种深度残差网络）两种不同的主干网络设置下,召回率和检测速度均超过了RPN（Region Proposal Network,区域提议网络）算法。提出的多分辨率图像目标检测算法能有效地提高检测精度,降低计算复杂度。     

数控车削编程系统图素工艺信息自动输入模块的研究

根据NC车削自动编程系统的需要,为高效输入图形零件信息,实现数控编程的自动化,确定了基于回转体零件的数据结构(包括几何信息和工艺信息).通过求交算法实现各图素的特征提取和输入,客户可在屏幕上点选特征图素并同时输入其对应的工艺信息,随后通过动态绑定技术利用数据库来进行数据处理和储存.不同的特征图素将产生不同类型的表,可通过打开数据库的方式使零件图形输入并重现,最终实现输出系统所需的数据结构,提高了图形输入效率,为虚拟化数控加工仿真提供技术支撑.