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.     

空战机动动作库设计方式研究

具体阐述了两类常见空战动作库的设计 :典型战术动作库和基本操作动作库 ,详细分析了这两类空战机动动作库的不足 ,最后提出了改进动作库设计的具体意见和建议。     

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。     

一种在线神经网络在补偿飞机模型不确定性误差中的应用

讨论了一种基于神经网络动态逆的直接自适应控制方法，并应用于超机动飞机的飞行控制中。基本控制律采用非线性动态逆方法进行设计，对由于模型不准确导致的逆误差采用单隐层神经网络进行在线补偿。仿真结果表明，神经网络通过补偿由于模型不准确引起的逆误差，弥补了非线性动态逆要求精确数学模型的缺点，提高了整个控制系统的鲁棒性，而且可以大大简化动态逆控制律的设计。     

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。     

一种基于误差四元数的战术导弹垂直发射姿态调转控制器

针对战术导弹垂直发射姿态调转时的快速性要求，研究了垂直发射快速姿态调转的控制问题。首先基于误差四元数并结合垂直发射的具体特点，建立了战术导弹垂直发射的非线性数学模型；然后通过李亚普诺夫第二方法进行控制系统设计，得出了基于误差四元数反馈控制器。分析了系统的稳定性和鲁棒性。该控制器实现了绕欧拉特征轴的旋转，缩短了姿态调转的时间，最后通过仿真验证其有效性。     

轮控小卫星姿态大角度机动递阶饱和控制器设计

针对采用反作用飞轮的小卫星姿态大角度机动控制，在反作用轮输出力矩受限、速率饱和的约束条件下，采用递阶饱和方法，即限制卫星每次姿态机动的最大偏差，对姿态偏差进行逐次消除。在毋需获知最优机动轨迹规划的情况下，可用于卫星任意时刻的姿态捕获和机动控制。数学仿真结果表明，本文设计的控制算．去能够实现快速姿态机动任务，具有良好的鲁棒性。