机载前视SAR三维成像原理及分辨率分析

提出一种新型的采用天线阵模式的机载前视合成孔径雷达(SAR)系统,分析了其三维成像原理。根据前视SAR的成像几何模型和回波信号特点给出了实现前视SAR三维成像的处理流程,详细推导了前视SAR的三维分辨率。模拟了X波段前视SAR点目标回波并进行了三维成像实验。仿真结果表明前视SAR系统能够实现三维成像,验证了本文算法的有效性。     

论弗雷格区分“a=a”和“a=b”失败

从柏拉图到蒯因,哲学家们提出各种方案证明＂a=a＂和＂a=b＂没有区分。这些证明尽管没有逻辑错误,但是违反经验,还是有瑕疵。弗雷格通过赋予语词意义和指称,试图证明＂a=a＂和＂a=b＂在认知意义上有区分;但是,由于弗雷格允许意义不同的语词可以有相同的指称,他的努力还是功亏一篑,＂a=a＂和＂a=b＂依然没有区分。有关同一性问题的讨论,尽管弗雷格的回答符合经验,但是不如柏拉图等人的回答在逻辑上完备,没有矛盾。这是一个很无奈的结局。     

非均匀分布式星载雷达栅瓣抑制技术研究

分布式星载雷达突破了单星雷达受功率孔径积的限制,具有更远的探测距离;突破了单星雷达受天线基线长度的限制,具有更窄的波束宽度,但是会产生较多的孔径干涉栅瓣.文中研究了一种分布式星载雷达的栅瓣抑制方法,该方法采用遗传算法来确定超稀疏的非均匀分布式星载雷达阵列的位置矢量,利用个体的实值编码提高了遗传算法的计算效率.＃在给定卫星阵列基线长度和卫星间最小间距约束的条件下,通过仿真实验验证了该方法抑制栅瓣的有效性.卫星个数越多,栅瓣抑制效果越好.随着小卫星技术的发展以及小卫星成本的降低,文中基于非均匀分布阵列的栅瓣抑制方法对小卫星的分布设计具有一定的参考价值.     

雷达装备远程技术支援保障综合集成研讨厅设计

在开放的复杂巨系统的理论下研究装备远程技术支援保障体系,并运用其方法论--综合集成研讨厅体系研究解决目前所遇到的问题,将雷达装备远程技术支援保障体系构建成一个人机结合,以人为主的智能化综合集成研讨厅.文中提出了3层结构的雷达装备远程技术支援保障综合集成研讨厅分布式网络环境,并论述了各层的组成及功能;给出了研讨厅MAS组织结构模型,并进一步提出了基于多Agent和B/S结构的研讨厅系统模型,同时论述了研讨厅系统模型中各层功能及实现方式.     

基于融合熵特征的辐射源信号识别

针对相近雷达辐射源信号难以识别的问题,一种新的雷达辐射源信号识别方法被提出.该方法基于小波包分解,用主成分分析法融合含有信号类别特性的小波包重构系数特征,并将融合特征的能量熵和概率熵构成特征向量,基于支持向量机实现信号的分类识别.在较大信噪比(SNR)范围内,使用该方法能获得满意的正确识别率,当SNR为5 dB时,十分近似的线性调频信号正确识别率达到了91%,实验结果证实了该方法的有效性.     

变学习速率在线ICA算法在雷达信号分选中的应用

已有的独立分量分析(ICA)雷达分选方法多采用FastICA算法,FastICA算法是一种离线批处理ICA算法,缺乏实时分选能力.文中将一种变学习速率的在线ICA算法应用到雷达分选中,克服了FastICA算法无法实现在线实时分选的缺点;同时,算法能根据相依性测度所反映的信号分离的状态自适应地调节学习速率,平衡了传统在线ICA算法收敛速度和稳态误差之间的矛盾,从而使得采用这种ICA算法的雷达信号分选方法具有收敛速度快,分离效果好的特点.仿真实验验证了分选方法的有效性.     

对多普勒天气雷达的欺骗干扰方法分析

多普勒天气雷达是利用云雨等散射体中的粒子与雷达之间的相对运动所形成的多普勒效应而进行气象探测的一种PD雷达.本文首先对多普勒天气雷达的多假目标欺骗干扰进行了仿真分析;然后从多普勒天气雷达探测气象目标的基本原理出发,提出了一种针对多普勒天气雷达实施假目标欺骗的新方法.     

宽带雷达回波的高频电磁散射快速算法

提出一种将射弹跳射线法和等效边缘电磁流法与线性调频信号雷达信号处理过程相结合的高频电磁散射算法,该算法能够快速准确计算复杂电大尺寸目标的大时宽带宽雷达回波信号.利用宽带雷达回波计算结果,获得了目标的一维距离像和二维ISAR成像,验证了方法的正确性.     

The Sichel model and the mixing and truncation order

The analysis of word frequency count data can be very useful in authorship attribution problems. Zero-truncated generalized inverse Gaussian–Poisson mixture models are very helpful in the analysis of these kinds of data because their model-mixing density estimates can be used as estimates of the density of the word frequencies of the vocabulary. It is found that this model provides excellent fits for the word frequency counts of very long texts, where the truncated inverse Gaussian–Poisson special case fails because it does not allow for the large degree of over-dispersion in the data. The role played by the three parameters of this truncated GIG-Poisson model is also explored. Our second goal is to compare the fit of the truncated GIG-Poisson mixture model with the fit of the model that results from switching the order of the mixing and truncation stages. A heuristic interpretation of the mixing distribution estimates obtained under this alternative GIG-truncated Poisson mixture model is also provided.     

A new double sampling control chart for monitoring process mean using auxiliary information

Statistical quality control charts have been widely accepted as a potentially powerful process monitoring tool because of their excellent speed in tracking shifts in the underlying process parameter(s). In recent studies, auxiliary-information-based (AIB) control charts have shown superior run length performances than those constructed without using it. In this paper, a new double sampling (DS) control chart is constructed whose plotting-statistics requires information on the study variable and on any correlated auxiliary variable for efficiently monitoring the process mean, namely AIB DS chart. The AIB DS chart also encompasses the classical DS chart. We discuss in detail the construction, optimal design, run length profiles, and the performance evaluations of the proposed chart. It turns out that the AIB DS chart performs uniformly better than the DS chart when detecting different kinds of shifts in the process mean. It is also more sensitive than the classical synthetic and AIB synthetic charts when detecting a particular shift in the process mean. Moreover, with some realistic beliefs, the proposed chart outperforms the exponentially weighted moving average chart. An illustrative example is also presented to explain the working and implementation of the proposed chart.