Manifold structure preservative for hyperspectral target detection

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

激光陀螺稳频反射镜的选择

讨论了球面及平面两种稳频反射镜的加工误差对陀螺腔内光路的偏移作用， 分析了腔内光路随这两种反射镜在稳频平移过程中的变化规律以及光路不对称变形对陀螺朗缪尔零漂的影响。分析结果表明： 无论从稳频反射镜存在加工误差考虑， 还是从稳频平移对陀螺性能影响的考虑， 选择球面镜作稳频反射镜要优于平面镜。     

基于DRFM的引信干扰技术研究

根据脉冲测距引信的工作原理和DRFM的基本功能,设计了DRFM结构,DRFM通过对脉冲延时,达到对脉冲测距引信进行干扰使其早炸的目的,并提出了延时参数的确定原则。     

故障检测与分离的广义一致性算子法

本文提出了故障检测与分离的广义一致性算子法的一般形式。得出了两个故障可分离的充要条件和多个故障可分离的充要条件。最后给出了检测和分离故障的过程。     

正—反激组合式变换器与单端式变换器的比较

以５０Ｗ变换器为例，分别从磁性元件的大小，功率管上的电应力、磁恢复问题，输出纹波及输出特性等方面，详细分析比较了组合式变换器与单端式变换器的优缺点，并给出了具体数据，提出了正－反激组合式变换器具有电压应力低，磁性元件小，体积小，重量轻，输出纹波小及可靠性高的结论。     

二维通道内湍流边界层分离的实验研究

用激光多普勒技术测量了二维扩压器中不可压湍流边界层分离流动，得到了时均速度和雷诺剪应力分布。实验结果分析表明：以Ｃｏｌｅｓ速度律发展的Ｂａｒｄｉｎａ速度分布可以描述瞬时间歇分离点以前和瞬时间歇再附点以后的时均速度分布，但无法描述分离区的边界层速度型。Ｃｒｏｓｓ速度分布可描述分离区的边界层速度分布。Ｃｅｂｅｃｉ＆Ｓｍｉｔｈ涡粘性代数模型难以正确地描述分离边界层的雷诺剪应力。     

神经网络在未来超分辨雷达中的应用

最新的技术发展已为研制超分辨雷达创造了条件。它能够突破普通雷达基于匹配滤波原理的分辨能力,而实现超分辨。超分辨雷达的巨大计算量通常来自求解非线性最小二乘问题时的多维搜索。本文针对这一问题,研究了雷达信号处理中的两个典型例子:(1)利用阵列处理检测个数已知而方向未知的雷达目标;(2)对波音727飞机进行超分辨距离多普勒成像。说明Hop-field神经网络通过集体运算能够求解各种困难的最优化问题,因而在未来的超分辨雷达中有广阔的应用前景。     

一种适于动态环境的盲多用户检测算法

比较两种盲多用户检测算法在动态环境中的收敛性能,并通过计算机进行模拟仿真。其中一种是能随输入信号矢量变化而变化的变步长最小输出能量盲算法;另一种是既保持最小输出能量检测器的全局收敛性,又具有最小均方误差检测器高输出信干比优点的判决反馈变步长盲算法。利用前者在动态强干扰下收敛快及后者在稳态时输出信干比高的优势,将二者结合起来应用到动态环境中,提高系统性能,同时抑制多址干扰和窄带干扰。     

机载脉冲多普勒跟踪雷达仿真系统

介绍了机载脉冲多普勒雷达仿真系统实现的基本技术思路,并从数据流处理的角度,阐述了仿真系统中部分仿真模型的实现方法,最后结合实例给出了仿真系统的测试结果数据,结果表明该系统为雷达干扰及抗干扰技术的研究提供了一个良好的数字仿真试验平台.