1种测量调幅失真的新方法

传统方法测量调幅失真都是用失真度仪或频谱分析仪，本文介绍了１种新方法，即用调制线性进行调幅失真的测试。     

地月低能转移轨道设计方法研究

研究地月低能转移轨道的设计方法。这种低能转移轨道利用了弱稳定边界理论，通过太阳的引力摄动，使得探测器能够不经过减速就被月球俘获。与经典的霍曼转移相比，低能转移轨道呵节省约140m／s的速度脉冲。由于设计是基于叫体问题模型进行的，问题具有很强的非线性特性，寻找满足约束条件的转移轨道变得非常困难。常用的两点边值问题的解法在这里都失效。本文在研究地月低能转移轨道特忡的基础上，对一般地月转移轨道搜索的变步长爬山法进行改进，用来设计地月低能转移轨道。仿真结果验证了该方法的有效性。     

复合材料结构件固化成工艺操作及控制

文章介绍了复合材料结构件固化设备、固化工艺、操作及控制 ,同时介绍了热压罐固化设备使用中的技术改造等内容。     

状态转移矩阵及系统响应函数

本文分析了系统状态转移矩阵及函数矩阵L,M的性质。给出了新的彼此转换关系。推导了系统的输入信号响应函数,及积分响应函数的表达式,说明了它们的计算方法。     

非均质变截面折线形梁面内振动的传递矩阵解法

考虑到折线形梁面内二维振动的特性，用传递矩阵法分析其面内的自由振动问题，导出了匀质变截面折线形梁面内振动的频率方程，利用非线性代数方程求根的计算机解法，可求得任意精度的任意阶固有频率。方法公式简洁，占计算机内存少，可编制成通用程序在小型微机上分析大型复杂折线形梁的面内振动特性。     

卫星导航信号监测技术

卫星导航信号监测技术是导航系统监测站的核心内容。文中介绍一种实时性强、处理精度高、可靠性好的导航信号监测技术,对监测接收机输出的实时观测数据和导航信号进行分析、处理,给出其合理性、完好性和定位精度分析,同时输出该原始数据。通过在卫通设备上实现伪距精密测量、设备时延精确校定,监测站能够配合导航系统进行站间时间同步,同步精度小于3ns。     

考虑机翼弹性变形时的传递对准方法研究

传递对准时 ,飞机机翼的弹性变形会对挂在机翼上导弹的子惯导系统的对准精度产生很大的影响 ,因此需要对弹性变形进行建模。此时 ,KainandCloutier提出的“速度 +姿态”匹配的方法已不再适用。本文提出了一种“速度+角速度”匹配的快速传递对准方法 ,并且采用了一种更符合实际飞行情况的仿真方法对安装误差角和弹性变形角进行了估计。结果表明 ,这种匹配方案的对准精度比“速度 +姿态”匹配的对准精度提高了至少一个量级以上 ,而且可以达到要求的精度     

一种更精确的带有杆臂补偿的速度加姿态匹配传递对准模型

针对捷联惯导系统的传递对准问题,提出了一种更精确的带有杆臂补偿和速度差乘积项的速度加姿态匹配的传递对准模型。对准模型中增加杆臂补偿项提高了系统的稳态精度;增加速度差乘积项改善了系统的动态响应,加快了滤波器收敛速度。采用“摇摆机动”的飞行轨迹,对传递对准滤波器进行了仿真;仿真结果证明了传递对准模型的有效性。     

Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions

Back propagation artificial natural network (BPANN) is a well known and widely used machine learning methodology in the field of remote sensing. In this paper an attempt is made to retrieve the spinach crop parameters like biomass, leaf area index, average plant height and soil moisture content by using the X-band scattering coefficients with BPANN at different growth stages of this crop. The maturity age of this crop was found to be 45 days from the date of sowing. After 45 days from the date of sowing, this crop was cut at a certain height for production. Then, it is a point of interest to investigate the microwave response of variation in production. Significant variations in all the crop parameters were observed after cutting the crop and consequently made the problem more critical. Our work confirms the utility of BPANN in handling such a non-linear data set. The BPANN is essentially a network of simple processing nodes arranged into different layers as input, hidden and the output. The input layer propagates components of a particular input vector after weighting these with synaptic weights to each node in the hidden layer. At each node, these weighted input vector components are added. Each hidden layer computes output corresponding to these weighted sum through a non-linear/linear function (e.g. LOGSIG, TANSIG and PURLIN). These functions are known as transfer functions. Thus, each of the hidden layer nodes compute output values, which become inputs to the nodes of the output layer. At nodes of output layer also a weighted sum of outputs of previous layer (hidden layer) are obtained and processed through a transfer function. Thus, the output layer nodes compute the network output for the particular input vector. In this paper, output nodes use linear transfer function. Different transfer functions e.g. TANSIG, LOGSIG and PURELIN were used and the performance of the ANN was optimized by changing the number of neurons in the hidden layers. The present analysis suggests the need of critical analysis of the BPANN in terms of selection of the best transfer function and other network parameters for the better results.