基于网格自适应的飞行器防热材料热传导系数辨识

为准确辨识高超声速飞行器防热材料热传导系数，构造了针对热传导偏微分方程系统的约束泛函极值问题，基于表征多项式逼近性能的Weierstrass定理，采用Lagrange多项式对热传导系数进行参数化建模，进而将无穷维的约束泛函极值问题转化为有限维的非线性规划问题，再利用基于动态优化方程的优化方法将此非线性规划问题转化为常微分方程初值问题进行求解。为从较有限的测量数据中准确地“学习”热传导系数，建立采用“过拟合”判别准则的网格自适应迭代算法改善辨识精度。研究表明本文采用的辨识策略与优化方法有效，辨识结果可以较准确地反映材料热传导系数的变化规律。     

A star identification algorithm based on radial and dynamic cyclic features of star pattern

A full-sky star identification algorithm based on radial and dynamic cyclic patterns is presented with the aim of solving the “lost-in-space” problem. The dynamic cyclic pattern match is applied with a maximum cumulate comparison method to identify sensor-catalog pairings in initial match, which substantially eliminates the effects of the star position noise, magnitude noise, and false stars. After initial match pairings of stars are obtained, a chain part extension technique is employed to quickly search for the longest match chain as the final result. Experimental results indicate that the proposed algorithm is highly robust to star position noise, magnitude noise and false stars. In a series of simulations, the identification rate of the algorithm is 97.50% with 2.0 pixels star position noise, 96.90% with 0.4 Mv star magnitude noise and 95.30% with four false stars respectively. Moreover, the algorithm achieves an identification rate of 58.08% when only six stars are in the field of view.     

A global solution for robust parameter design of aeronautical electrical apparatus based on interactions analysis and polynomial fitting

Robust Parameter Design (RPD) has been widely applied for improving quality and reliability of products. One of the key drawbacks of applying RPD using Taguchi method is that the stable factors may not be independent of the adjustment factors, resulting in unsatisfactory design. Moreover, the Taguchi method cannot guarantee global optimality since the levels set in the experiment are usually discrete to ensure orthogonal design. In this paper, robust solutions of the stable factors are obtained via a nonlinear model based on polynomial fitting; while the adjustment factors are obtained via interactions analysis so that they are independent of the stable factors. In particular, the values of the adjustment factors are determined by output offset compensation so as to achieve robustness of the design scheme. An example on the design of an aeronautical electrical apparatus is presented to illustrate the procedure. The results show that the proposed method can take full advantage of the nonlinearity in the response and achieve the desired outcome.     

Space object material identification method of hyperspectral imaging based on Tucker decomposition

Space object material identification method based on Tucker decomposition is proposed and demonstrated experimentally. Space target generally has a low spatial resolution because of the limitation in detection distance. Hyperspectral imaging (HSI) technology can capture the image from visible light to shortwave infrared continuously while providing the spatial and spectral information of the target, thereby introducing a new space target detection and identification approach. However, the data obtained by the HSI system often contain mixed pixels, thereby causing difficulties in target material identification. In this work, a material identification method of hyperspectral data based on Tucker decomposition is proposed by combining mixed spectral theory with tensor decomposition. The feasibility of the method is verified by using satellite model hyperspectral data with different spatial resolutions compared with the endmember obtained from the non-negative matrix decomposition (NMF), independent component analysis (ICA), tensor singular value decomposition (t-SVD). The average CORR of NMF, ICA, t-SVD and the proposed method is 0.2694, 0.5818, 0.6397 and 0.937, correspondingly. Therefore, the proposed method has demonstrated a more remarkable performance in terms of material identification, the analysis results of the material abundance distribution that used the proposed method.     

基于希尔伯特-黄变换的雷达数据误差修正处理技术

由于雷达跟踪测量数据总存在误差,而目前采用的误差处理算法修正效果并不理想,为了有效估计数据中的未知误差,提高数据处理的精度,提出了一种基于希尔伯特-黄算法的雷达误差残差诊断方法,利用得到的内蕴模态函数拟合出误差残差的特征,准确分离出设备的误差信息。通过对某雷达的实测数据分析证明该方法较其他方法对解决该类问题更为有效,且在事后高精度数据处理中具有较好的实用性。     

基于角动量守恒的空间机器人动力学参数辩识

空间机器人由于加工、装配误差以及在轨燃料消耗等因素使其名义公称参数与实际 动力学参数相比存在一定的误差。空间机器人路径规划和地面机器人不同,其广义雅克比 矩阵包含动力学参数,使计算的轨迹偏离实际要求的路径,引起末端位姿误差。本文根 据自由飘浮空间机器人的角动量守恒方程式,对一种两自由度空间机器人的基座以及机械臂 的各个关节分别单独做三次多项式轨迹激励,利用基于偏差模型的最小二乘法和遗传算 法对动力学参数进行辨识。仿真结果表明,遗传算法的计算稳定性和对动力学参数辩识精 度优于最小二乘法。〖JP〗     

基于单目视觉的空间非合作目标相对运动参数估计

提出了一种估计空间非合作目标相对运动参数的单目视觉方法,该方法将相对运动参数估计问题描述为一个非线性约束优化问题．通过迭代求解特征值问题来确定基本矩阵,将基本矩阵分解可得到相对运动参数的4个可能解,给出了消除旋转矩阵多义解的简单判断准则．数值仿真结果验证了该方法的有效性．     

基于PEA的椭圆轨道航天器编队飞行高精度位置保持

针对航天器编队飞行时相对位置保持精度要求高的特点,采用基于线性变参数模型的多项式特征结构配置方法设计椭圆轨道航天器间相对位置控制算法。对于线性变参数控制系统,本文将基于线性定常系统的多项式特征结构配置方法推广以确保系统性能与变化参数间的独立性。在特征结构配置时,先设计带参数变化的控制器结构后计算出带未知控制器增益的设计闭环传递函数,接着将其与含有闭环系统性能的期望传递函数在三个条件下进行匹配,进而获得未知控制器增益的表达式。在设计实际控制椭圆轨道编队飞行MIMO相对位置保持算法时,将系统期望传递函数设为解耦形式来实现三轴位置控制间的解耦控制,达到提高系统控制性能的目的。最后进行相应的数学仿真,其结果表明该算法能够保证系统的高精度位置保持要求。     

基于双目视觉的相对状态自主确定

基于双目视觉系统提出了一种适于交会对接最后阶段的近距离绕飞的相对状态自主确定方法。用修正罗德里格参数(MRP)描述姿态,给出了追踪器与目标器间的相对姿态运动学与动力学方程,以及相对运动方程,根据双目视觉系统的小孔成像原理建立了双目系统的测量数学模型,由扩展卡尔曼滤波(EKF)获得两航天器的相对位姿及其变化率。仿真结果表明该法可行。     

舰载飞机弹射起飞的机舰参数适配特性

舰载飞机与航母之间的参数适配特性是弹射起飞总体方案设计阶段的关键问题之一。针对舰面起飞的复杂环境,建立了舰载飞机弹射起飞的数学仿真模型。在此基础上,分析了各主要机舰参数对弹射起飞安全性的影响,并计算得到满足弹射起飞安全准则的主要机舰参数的适配值集合。研究结果表明:在弹射起飞过程中,增加弹射能量、前起落架突伸力和升降舵预置偏角均可抑制航迹下沉;在适配值集合内,随着弹射能量的增加,升降舵预置偏角的适配范围增大,前起落架突伸力的影响减小。