基于小波变换和ICA的卫星测控信号盲识别算法

针对信息对抗中对非合作卫星测控信号识别的问题,提出一种小波变换和独立分量分析相结合的卫星测控信号盲识别算法.首先,基于小波变换去除噪声原理,对混合信号进行去噪处理;然后,采用独立分量分析的方法对去噪后的混合信号进行分离;最后进一步对分离后的测控信号作矢量归一和再去噪处理,得到卫星测控信号的最终估计.Matlab仿真结果表明:该识别算法可以较好地分离卫星测控信号,并且很好地保留了源信号的特征,具有良好的稳定性,收敛速度较快.     

过程质量控制新方法研究

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空间智能天线夹芯结构热变形力学分析

空间智能天线夹芯结构是一种集承载、维形和微波通讯于一体的多功能结构。该结构将微带天线阵列植入复合材料夹芯结构蜂窝芯体中,既满足了结构力学和热力学性能要求,又满足了被植入天线的电性能要求,实现了空间结构的多功能化,降低了系统综合质量。本文针对低地球轨道的空间热环境,选取低热膨胀系数、低密度、高强度、高模量、耐高温及透波性好的复合材料,基于复合材料力学经典理论分析设计了面内热膨胀系数接近于零的复合材料蜂窝夹芯结构面板,并且对空间智能天线夹芯结构在低地球轨道典型空间环境下的结构热力学响应进行了有限元分析。结果表明,该空间多功能智能天线夹芯结构可以满足在轨空间温度环境下结构变形指标要求。     

基于ASAAC标准的无人机地面站系统管理方案的设计与实现

相比于飞机系统,无人机地面站的系统设计,还欠缺相应的指导标准和设计规范。因此,无人机地 面站设计过程中,参考借鉴飞机系统的设计标准与规范,来提高无人机地面站的稳定性和可用性,是一种可行 的设计思路。本文参考了ASAAC 标准,设计并实现了一种分层级的地面站系统管理方案。     

Compliance of disposal orbits with the French Space Operations Act: The Good Practices and the STELA tool

Space debris mitigation is one objective of the French Space Operations Act (FSOA), in line with Inter-Agency Space Debris Coordination Committee (IADC) recommendations, through the removal of non-operational objects from populated regions. At the end of their mission, space objects are to be placed on orbits that will minimize future hazards to space objects orbiting in the same region. The FSOA, which came into force in 2010, ensures that technical risks associated with space activities are properly mitigated. The Act confers CNES a central support role in providing technical expertise to government on regulations dealing with space operations. In order to address the compliance of disposal orbits with the law technical requirements, CNES draws up Good Practices as well as a dedicated tool, Semi-analytic Tool for End of Life Analysis (STELA).     

Cooperation and dialogical modeling for designing a safe Human space exploration mission to Mars

This paper proposes an approach for a complex and innovative project requiring international contributions from different communities of knowledge and expertise. Designing a safe and reliable architecture for a manned mission to Mars or the Asteroids necessitates strong cooperation during the early stages of design to prevent and reduce risks for the astronauts at each step of the mission. The stake during design is to deal with the contradictions, antagonisms and paradoxes of the involved partners for the definition and modeling of a shared project of reference. As we see in our research which analyses the cognitive and social aspects of technological risks in major accidents, in such a project, the complexity of the global organization (during design and use) and the integration of a wide and varie d range of sciences and innovative technologies is likely to increase systemic risks as follows: human and cultural mistakes, potential defaults, failures and accidents. We identify as the main danger antiquated centralized models of organization and the operational limits of interdisciplinarity in the sciences. Beyond this, we can see that we need to take carefully into account human cooperation and the quality of relations between heterogeneous partners. Designing an open, self-learning and reliable exploration system able to self-adapt in dangerous and unforeseen situations implies a collective networked intelligence led by a safe process that organizes interaction between the actors and the aims of the project. Our work, supported by the CNES (French Space Agency), proposes an innovative approach to the coordination of a complex project.     

干摩擦对称黏滑响应的级数解及黏滑边界特性

针对航空发动机中的摩擦振动问题,给出了对称黏滑运动摩擦力的分段表达式.利用谐波平衡方法推导了具有偶数次黏着的黏滑运动的级数形式解析解,并基于该解析解给出了参数平面内的黏滑边界线方程.黏滑运动响应仅包含奇次谐波成分,在奇次分数倍频处存在超谐次共振.当频率比小于0.4时,黏滑边界线呈现凸凹交替特征,局部极小值位于各阶超谐次共振频率处.黏性阻尼增大能够缩小无黏着运动的参数范围,但在各阶超谐共振频率附近,黏性阻尼增大会扩大无黏着运动的参数区域.      

高轨非合作目标多视线分布式相对导航方法

针对仅采用单视线难于实现高轨非合作目标的远距离自主相对导航问题,提出基于双空间机器人的多视线分布式相对导航方法。首先,建立基于多视线测量的相对导航模型,并给出空间机器人与非合作目标的相对动力学模型;然后,为降低集中式滤波相对导航的维数,设计分布式滤波的相对导航方法,采用扩展卡尔曼滤波算法实现相对运动估计;最后,建立数学仿真模型,对多视线分布式滤波相对导航算法及可观测性进行仿真校验,仿真结果表明该方法是稳定有效的。     

利用时延神经网络的动载荷倒序识别

将时延神经网络引入动载荷识别研究中,结合时延神经网络的"记忆"特性、因果有限长冲激响应（FIR）系统理论与振动响应的求解原理,提出一种利用时延神经网络的时域动载荷倒序识别方法。对一个受两点随机动载荷作用的舵面模型结构进行载荷识别验证实验,结果表明,用本文方法识别的两个激励点上识别载荷样本的时间序列与真实载荷样本的时间序列之间的均方根误差分别为0.635 4和2.543 7,识别载荷样本时间序列与真实载荷样本时间序列的相关系数分别为0.965 7和0.826 2,功率谱密度曲线也能够较好吻合。本文提出的方法具有不需要结构动力学模型、识别精度高的优点。