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.     

舰载机着舰时航迹稳定性研究

本文讨论了舰载飞机以小于有利速度着舰时的航迹稳定性问题。在分析飞机航迹稳定性的同时,还对油门杆控制、机动襟翼控制对改善航迹稳定性的作用进行了初步探讨;在此基础上还讨论了舰载飞机加装波束导引系统对着舰过程的影响原理。结果表明:采用油门杆控制、机动襟翼控制来改善着舰阶段的航迹稳定性是可行的:而且用以上两控制系统作为内环的自动驾驶仪在波束导引系统自动导引飞机着舰过程中,能控制飞机的飞行速度、高度,帮助驾驶员操纵。     

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。     

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。     

某无人直升机扩频通信系统捕获方案设计

讨论一种用于某无人机直序扩频通信测控系统的PN码捕获方案。根据该系统的技术指标要求,针对无人机信道的特殊性,采用匹配滤波器进行PN码的相关处理,并运用检测后积分和二次驻留的方法提高检测概率降低虚警概率。在进行详细理论分析的基础上,运用Cadence公司的SPW系统仿真软件,将该系统在高斯白噪信道和无人机信道下进行对比性系统仿真,仿真结果以及FPGA实测结果均表明,上述两种方法有效地提高了系统性能。     

基于偏差信息的星载计算机系统负载平衡算法的研究

相邻结点负载平衡算法具有计算简单、额外开销小的优点。但由于采用局部的观点平衡负载，限制了结点信息得到的范围，使得系统在某种情况下达不到全局平衡，本文提出了基于偏差信息的星载计算机系统负载平衡算法，它改变了结点传递的实际负载信息，使修改后的负载信息能体现出结点及其周边结点的负载形势，以此弥补了相邻结点负载平衡算法的不足，并将其应用在星载计算机系统上，首先，介绍了相邻结点负载平衡算法及其存在的不足；其次，描述了基于偏差信息的星载计算机系统负载平衡算法，最后，通过仿真实验，给出了这种算法同其它几种负载平衡算法的比较，并证明了它的正确性与可行性。     

基于鲁棒故障检测滤波器的传感器故障诊断

本文讨论了鲁棒故障检测滤波器在喷气式发动机传感器故障诊断中的应用。鲁棒故障检测滤波器方法使残差既有干扰解耦特性又有方向特性。该滤波器的设计结合了未知输入观测器原理与Beard故障检测滤波器原理。利用未知输入观测器产生的残差对未知输入(干扰)具有鲁棒性,而特定方向性是基于BFDF技术获得的。最后举例说明了该方法的可行性和有效性。