军用涤纶长丝面料热转移印花及功能整理研究

介绍涤纶长丝面料分散染料热转移印花工艺,包括工艺流程、主要技术创新、产品应用等方面。阐述涤纶长丝面料阻燃涂层整理技术,并讨论阻燃剂用量、涂层增重量对整理效果的影响。总结防热红外侦察整理、防雷达波侦察整理、多功能迷彩面料生产及要点。文章表明,化纤长丝纺织产品的发展与国防建设是密切相关、相互促进、相辅相成的。以化纤长丝为基材生产的系列化军用产品是建设强大国防的重要保障。不断深化化纤长丝面料的功能性深度加工具有非常重要的意义。     

栈式降噪自编码器在波形单元识别中的应用

为提高多功能雷达(Multi-Function Radar,MFR)波形单元的识别准确率和鲁棒性,提出一种栈式降噪自编码器(Stacked Denoising Autoencoders,SDAE)与支持向量机(Support Vector Machine,SVM)相结合的波形单元识别方法.首先摒弃传统依赖脉冲序列分析技术对MFR信号进行处理的方法,通过分析波形单元结构并借助参数间的联合变化特征,提出一种MFR波形单元分段识别模型,将传统对脉冲序列的识别转化为对MFR波形单元的识别;然后在该模型的基础上引入SDAE,对训练样本数据、SDAE隐含层神经元节点进行加噪处理,并利用这些加噪后的样本数据训练优化SDAE网络模型,提取出样本数据的深层稳健特征;最后引入SVM算法,借助SDAE挖掘出的样本深层特征,实现SVM模型的优化,得到最终的波形单元识别模型(SDAE-SVM).仿真实验表明:提出的波形单元识别方法在相同样本数目和测试误差的条件下,与SVM算法相比,能够取得较高的识别准确率,具备更优越的识别效果.证实MFR波形单元识别模型是有效的,且通过SDAE网络的引入,使得SDAE-SVM方法能够自主地挖掘原始信号的深层特征,提高波形单元识别的鲁棒性和准确率.     

压缩感知合成孔径雷达射频干扰抑制方法

压缩感知合成孔径雷达成像技术能够利用少量采样数据恢复目标图像,大大减少数据存储系统的负担,但受到射频干扰时成像品质会严重下降;在目标场景稀疏条件下,分别构建目标回波信号原子库和射频干扰原子库,设计了压缩域合成孔径雷达成像射频干扰抑制算法,并对该算法进行理论分析,最终通过仿真实验验证了算法的有效性;仿真结果表明：该算法在较大干信比条件下,能有效抑制合成孔径雷达射频干扰,达到较好的成像效果。     

雷达导引头最优控制问题研究

建立雷达导引头角跟踪控制数学模型,运用最优控制理论推导天线控制指令;基于机动目标当前统计模型,运用卡尔曼滤波及多普勒跟踪速度修正信息,估计出天线最优控制状态量;经仿真验证,设计方法不仅能够改善制导律输入精度,同时能够提高雷达导引头的响应性能.     

探地雷达在路面厚度无损检测中的应用

介绍了探地雷达检测路面厚度的基本原理、厚度计算方法和探地雷达对路面结构层厚度检测的过程,根据传统检测方法和运用探地雷达检测结果的比对,证明了探地雷达检测路面厚度在实际中是切实可行的。     

基于地质雷达技术的房屋周边缺陷探查

房屋周边的开裂、空洞、塌陷缺陷,影响着房屋的美观与使用。本文结合实际工程,对房屋周边的外露缺陷进行了检查记录,并运用地质雷达技术对房屋周边没有外露缺陷的位置进行了缺陷探查,亦探查出了下方存在明显的缺陷。该技术为快速普查房屋周边缺陷提供了一个好的解决方法。     

Sea clutter modeling using an autoregressive generalized nonlinear-asymmetric GARCH model

The sea clutter modeling is critical to the radar design and assessment of relevant detection algorithms. In this paper, we investigate a family of generalized autoregressive conditional heteroscedastic (GARCH) processes to model the sea clutter as a time series, in which the current variance is dependent on historical information. The most general model (so-called the ALLGARCH model) provides more flexible variance structures to model non-Gaussian, asymmetry, and nonlinear properties of the clutter. However, after going through the usage of the ALLGARCH model, we find that it is not very suitable because the coefficients of the model, which are numerous, would be difficult to estimate in a real-time operating environment. Meanwhile, we find that some of the coefficients are negligible under almost all kinds of sea environments and weather conditions. Motivated by these observations, we propose a novel GARCH model for sea clutter modeling, which is a generalization of the nonlinear-asymmetric GARCH (NAGARCH) model. Considering the correlation between adjacent clutter returns, autoregressive terms are also introduced. By systematically analyzing practical sea clutter data under different sea environments, we demonstrate that the proposed model achieves comparable fitting effect to some commonly used statistical models. Also, we develop the corresponding generalized likelihood ratio test (GLRT) algorithm for the new model. Numerical simulations exhibit that the proposed detector achieves higher probability of detection, comparing with the AR-GARCH detector.     

卫星雷达测高的应用现状及发展趋势

进入新世纪以来,卫星雷达测高技术迅速发展,部分领域已进入业务化实施阶段,目前表现出了新的发展趋势。详细介绍了卫星雷达测高在海洋、陆地水体、冰川和测绘上的应用,并对目前测高技术存在的一些问题进行了分析。我国在雷达测高领域与西方发达国家还有很大差距,及时加强在该领域的研究非常必要。
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Utility detection and positioning on the urban site Sense-City using Ground-Penetrating Radar systems

This paper presents the design of a novel Ground-Penetrating Radar (GPR) test site that has been integrated into the mini-city demonstrator Sense-City located at University Paris-Est (France). This test site provides several sources of measurement interest expressed by the presence of a multilayered soil with significant dielectric contrasts, and various dielectric pipes and blades buried at various depths in trenches filled with a backfill soil different from the natural soil. This paper presents experimental Bscans associated with the pipe zone acquired by three different GPR systems at frequencies ranging from 300 MHz to 1.5 GHz. The interpretation and comparison of the raw Bscans have allowed to characterize the dielectric properties of the soil layers, and to detect the hyperbola signatures of the buried pipes. The results of this study will help to guide future developments on polarization, operating frequency and signal processing to extract parameters (orientation, dielectric characteristics, position and size) associated with pipes.     

基于DRGAN和支持向量机的合成孔径雷达图像目标识别

为解决SAR图像目标识别中样本缺乏和方位角敏感问题,提出了一种基于DRGAN和SVM的SAR图像目标识别算法。首先,采用多尺度分形特征对SAR图像进行增强,经过分割得到目标二值图像,基于Hu二阶矩估计目标的方位角。然后对估计得到的目标方位角进行量化编码,结合原始图像作为输入,对设计的DRGAN模型参数进行训练与优化。由于DRGAN中的深度生成模型将目标姿态与外观表示进行解耦设计,故可利用该模型将SAR图像样本变换到同一方位角区间。基于变换后的训练样本分别提取归一化灰度特征,利用SVM训练分类器。采用MSTAR数据集在多个不同操作条件下对提出的算法进行测试,实验结果表明,在带变体的标准操作条件下,能够达到97.97%的分类精度,优于部分基于CNN模型的分类精度,在4种扩展操作条件下的分类精度分别为97.83%,91.77%,97.11%和97.04%,均优于传统方法的分类精度。在SAR图像目标方位角估计存在一定误差的情况下,训练得到的GAN模型作为SAR图像目标旋转估计器,能够使得在不进行复杂样本预处理的前提下,仍然取得较高的SAR图像目标识别精度。