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.     

横航向驾驶员诱发振荡研究及地面模拟验证

本文建立了包括非线性环节在内的横航向驾驶员—操纵系统—飞机本体组合系统的数学模型,采用闭环控制原理,对JJ-7飞机横航向驾驶员诱发振荡PIO问题进行了研究,并详细地讨论了驾驶员参数及其各种操纵动作、操纵系统和飞机本体气动参数对横航向PIO的影响,探讨了横航向PIO产生的机理。文中以JJ-7飞机为例,采用时域法在整个飞行包线各点进行了检查,并用固基飞行模拟器进行模拟验证。其结果与理论计算结果相一致。     

域外刑事评议程序之比较研究

刑事评议程序是介于庭审程序和宣告程序之间的一个重要程序,它既是庭审的自然延续,又是裁判结论形成的前提和基础。然而,欲保证刑事评议程序的正常运行,刑事评议主体之独立必须得到保证;刑事评议之客体还应被全面考虑,并对其进行细化,以确保刑事评议的针对性和有效性,防止出现"估堆"现象。本文以域外刑事评议程序为对象,从评议的主体、客体等角度进行梳理,得出规律性的认识,以期对我国关于刑事评议的立法有所裨益。     

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。     

逆合成孔径雷达信号处理方法研究进展

在简要评述国内外现有的逆合成孔径雷达（ISAR）信号处理的运动补偿和成象方法之后，介绍了南京航空航天大学ISAR课题组近年来在ISAR信号处理方法研究中取得的主要进展，包括基于多散射点定位观点的雷达成象观测模型，基于最大似然原理的同时运动补偿和成象方法，基于最大似然原理的多目标成象方法和基于时频分析的多目标成象方法，以及超分辩成象方法，文末指出了在ISAR信号处理方面有待进一步研究的问题。     

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。     

低轨卫星面阵凝视成像技术研究

文章介绍了面阵凝视成像的工作原理及特点，阐述了低轨卫星对地观测凝视成像面临的像移问题及其解决措施，对像移补偿装置与二维指向机构的结合做了初步探讨。     

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。