二面角的双基地散射特性分析

二面角效应是高分辨率 SAR 图像非常重要的特征,其主要是建筑物的墙面和地面及其他人造物体形成的二面角造成的强反射。为了减小二面角的雷达散射截面积(Radar cross section,RCS) ,常用的技术是改变二面角的几何结构。双基地雷达是一种几何配置灵活的雷达,具有一定的反隐身能力。本文主要利用几何光学分析了不同张角二面角的散射特性及其SAR图像表现形式。针对二面角的多次散射问题,计算了散射次数和最终出射角度的通项公式,并给出了双基地条件下二面角RCS的定量计算公式。仿真结果表明,不同张角的二面角RCS和双基地雷达的几何结构密切相关,在特定的双基地雷达几何配置下,二面角在双基地SAR图像中 表现为角点处的强点。     

GNSS-R SAR地面目标建模与成像

针对GNSS-R SAR对地成像仿真中,由于地面目标建模以目标复杂的实地勘测DEM（Digital Elevation Model）为基础而导致的典型目标影像特征不利于单独观察的问题,基于蒙特卡罗方法对随机地表建模,基于欧几里德原理对建筑和树木建模,构建结构灵活可控的成像场景。利用导航卫星作为辐射源,机载接收机作为信号接收平台,成像场景反射的GNSS-R信号作为回波信号,BP算法作为成像处理算法,进行成像仿真。仿真结果表明,该仿真场景下生成的图像可反映不同地面目标的结构特点和散射强度差异,证明了所构建的地面目标模型能够应用于GNSS-R SAR成像系统的仿真验证。     

SAR图像数据稀疏性分析及在特征增强中的应用

利用合成孔径雷达(SAR)图像中目标的后向散射特性和目标散射中心的理论,分析了SAR图像数据稀疏性的成因。指出SAR图像的稀疏性与典型散射体的后向散射特性、成像区域目标强散射中心的稀疏性和成像区域的粗糙度等因素有关。根据视觉稀疏表示机制,比较了SAR图像与光学图像稀疏特征在视觉上的差异。然后,分析了稀疏约束在SAR图像分辨率增强中的应用方法。最后,用仿真和实测SAR图像数据验证了稀疏性在特征增强中的作用。     

基于雷达散射特性的复杂目标SAR图像仿真

高频区复杂目标宽带雷达特征信号仿真,尤其是两维SAR图像的仿真对雷达目标识别和SAR图像解译具有重要意义.根据复杂目标三维模型运用物理光学(PO)法与增量长度绕射系数(ILDC)法结合的改进图形电磁算法快速计算目标雷达散射截面,而后通过对不同SAR成像模式的研究建立SAR仿真成像系统,从而分别仿真得到StripSAR、SpotSAR、ISAR图像.仿真结果与真实图像相比具有较好的相似度,验证了方法的有效性.     

无相位检测电磁逆散射目标成像

提出一种新的无相位检测电磁逆散射成像方法,该方法基于测量的总场幅度首先构造价格函数,通过优化该价格函数复原散射场幅度和相位;由复原的散射场幅度和相位重建目标的几何参数和电磁参数.相位复原所需计算时间远小于对目标成像所需计算时间,不增加逆散射的计算成本,具有较强的实用性.在逆源方法中引入等效电流源降低了逆问题的非线性度,等效电流源非辐射分量的重建提高了目标重建的质量.文中首次用实验结果验证了电磁逆散射无相位检测的可行性,证明了方法的有效性和高度稳定性.     

基于切比雪夫多项式的双基地前视SAR成像算法

双基地前视合成孔径雷达(SAR)的特殊构型使其距离历程具有双根号和大斜视的特点,给回波信号的成像处理带来困难,也对成像算法的精度提出更高要求,为此提出一种基于切比雪夫多项式的双基地前视SAR RD成像算法.首先,建立双基地前视SAR的几何模型与回波信号模型,指出收发分离构型对二维频谱公式推导过程中驻定相位点求解的影响;然后,引入切比雪夫多项式展开代替传统成像算法中的泰勒级数展开,在此基础上进行双基地前视SAR斜距等效模型和二维频谱模型的推导;最后,通过分析确定双基地前视SAR RD成像算法的相位匹配函数,给出成像算法的完整流程.仿真结果表明,所提出算法可极大减小双基地前视SAR的斜距等效误差与频谱展开误差,有效提高成像算法的聚焦深度和成像质量.     

基于分布目标模型的SAR场景模拟

地理环境的场景回波模拟直接影响到合成孔径雷达(SAR)对抗系统仿真试验的效能评估.通过分析研究场景地表地物的电磁特性,利用高分辨率卫星影像数据的地物特征,采用面向对象的多尺度分类分割算法,提取得到了真实地物电磁特性数据.在构建分布目标模型时,先将实测数字高程模型(DEM)数据进行分形插值处理,建立几何模型,再利用小面单元模型建立散射模型,小面单元的电磁散射特性数据由与DEM数据对应区域的卫星影像数据提取得出,从而建立真实地理环境的SAR回波模型.通过ChirpScaling成像算法对回波信号进行处理,证明了方法的可行性.得到的场景回波模型与通常所用的忽略地物电磁特性得到的回波模型相比,具有逼真度高的优点,为SAR对抗系统仿真试验提供支持.     

城市目标高分辨率SAR遥感监测技术研究进展

高空间分辨率SAR卫星为城市监测提供了宝贵数据源。在高分辨率SAR图像上,目标的多维特征得以体现,可以大大提高对城市目标的识别能力。但由于城市目标的复杂性、密集建筑物间电磁波的多次散射,以及SAR成像固有的几何畸变与噪声的影响,使得城市目标在高分辨率SAR图像上的特征更为复杂。围绕城市目标电磁散射特性与SAR成像机理、城市目标精细结构提取与三维重建等内容,介绍了国内外相关的研究进展,指出了该领域未来的研究方向。     

合成孔径雷达(SAR)实时成像仿真研究

进行合成孔径雷达（SAR）成像仿真是研究成像雷达的一个重要手段。该文在分析SAR成像仿真机理的基础亡，提出了一种利用视景仿真软件Vega进行SAR实时成像的仿真方法。该方法在建立场景模型的基础卜，利fEH场景纹理材料的物理属性数据及雷达参数计算雷达散射截面（RCS），再把RCS值输入一个能模拟雷达真实设备的传感器模型，就得到SAR实时仿真图像。为了满足SAR成像仿真的需求，对Vega软件中的纹理材料数据库文件进行了修改，建立了新的纹理材料数据库文件。仿真结果表明新方法能较好地满足SAR实时成像仿真需求，有助于促进SAR成像实时仿真技术的发展。     

2～18GHz外场RCS测量系统的设计与实现

外场RCS测量是获取大型全尺寸目标电磁散射特性的重要手段。在介绍脉间频率步进雷达一维、二维成像原理的基础上,给出了一种宽带(2～18GHz)双通道雷达目标RCS测量系统的设计与实现。该系统可用于外场目标后向电磁散射特性测量和散射源高分辨力二维成像。介绍了系统的技术特点,讨论了影响外场RCS测量精度的几种主要误差来源并提出了解决办法,最后对系统性能进行了验证。     

Time–frequency analysis for moving ship targets in GEO spaceborne/airborne bistatic SAR imaging based on a GEO satellite transmitter

The track of a synthetic aperture radar (SAR) system mounted on a geosynchronous orbit (GEO) satellite can be curvilinear. GEO spaceborne/airborne bistatic (SA-Bi) SAR, which employs a GEO satellite as a transmitter and an aeroplane that works on the ‘receive-only’ mode as a receiver, is distinct from general bistatic SAR systems. During coherent integration time, the rotation of ship targets results in a time-varying Doppler frequency shift that produces a smeared Doppler spectrum and a blurred image after Fourier transform. This article mainly studies the time–frequency characteristics of ship targets in a GEO SA-Bi SAR system. First, the geometric model of a GEO SA-Bi SAR in ship target imaging is built, and the Doppler frequency shift function induced by target rotation is derived in detail. Then, the instantaneous frequency characteristics of moving targets are analytically calculated based on the time–frequency analysis method. Moreover, a rotation target imaging strategy of GEO SA-Bi SAR based on the time–frequency analysis method is proposed and tested with simulation data, which demonstrates the correctness and effectiveness of the proposed method.     

Stochastic models for recognition of occluded targets

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise. In this paper, we present a hidden Markov modeling based approach for recognizing objects in SAR images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabilistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.     

SAR图像建筑物目标检测研究综述

面对日益剧增的城市建筑物,合成孔径雷达(synthetic aperture radar,SAR)图像的建筑物检测作为SAR图像解译的一个分支逐渐成为一项重要的研究课题.对现有的研究方法进行了分类,从基于传统方法的建筑物检测和基于深度学习的建筑物检测两方面入手,对现有SAR图像的建筑物目标检测算法进行了梳理.简述了SA...     

A novel method for speckle noise reduction and ship target detection in SAR images

It is very difficult to detect small targets when the scattering intensity of background clutter is as strong as the targets and the speckle noise is serious in synthetic aperture radar (SAR) images. Because the scattering of man-made objects lasts for a longer time than that of background clutter in azimuth matching scope, it is much easier for man-made objects to produce strong coherence than ground objects. As the essence of SAR imaging is coherent imaging, the contrast between targets and background clutter can be enhanced via coherent processing of SAR images. This paper proposes a novel method to reduce speckle noise for SAR images and to improve the detected ratio for SAR ship targets from the SAR imaging mechanism. This new method includes the coherence reduction speckle noise (CRSN) algorithm and the coherence constant false-alarm ratio (CCFAR) detection algorithm. Real SAR image data is used to test the presented algorithms and the experimental results verify that they are feasible and effective.     

一种合成孔径雷达图像阴影和目标检测的方法

提出了一种进行高分辨率SAR(synthetic aperture radar)图像阴影和目标检测的新思路.首先给出了基于等效视数模型的图像RCS(radar cross section)重构算法进行阴影检测,然后通过非线性积累,达到目标检测的目的.与CFAR(constant false alarm rate)检测相比,能够检测到阴影和更多感兴趣的目标.     

基于谱归一化生成对抗网络的目标SAR图像仿真方法

为解决合成孔径雷达(Synthetic Aperture Radar, SAR)自动目标识别(Automatic Target Recognition, ATR)中的数据稀疏问题,提出一种基于谱归一化生成对抗网络(Spectral Normalization Generative Adversarial Network, SN-GAN)的目标SAR图像仿真方法。本文方法通过构建目标—场景—雷达耦合物理模型,求解散射强度分布图,利用SN-GAN实现对散射强度分布图的优化,生成高质量仿真SAR图像。通过3种相似性评估算法对仿真图像进行相似度评估,验证本文仿真方法的有效性。最后通过多组SAR ATR进行实验验证,在训练集中加入SN-GAN优化的仿真SAR图像可以有效缓解数据稀疏问题,提升分类算法的准确率。     

A SAR image method of detecting target changes using two-dimensional scattering centre characteristics

Remote-sensing image interpretations and applications require information on changes in the target. In high-resolution synthetic aperture radar (SAR) images, multi-scattering centres reflect the characteristics of target scattering, but not those of point targets or point scattering. Total scattering is the vector summation of each scattering centre. These scattering centres include shape and structural information of the target. When a target changes, both the scattering characteristics and the scattering centres change. In this way, changes in the centres may cancel out changes in the target. This article proposes a new method of change detection for SAR image targets using the two-dimensional scattering centre characteristics (TDSCC). This method is here called the TDSCC algorithm. This algorithm differs from other change detection algorithms that are based on image fields. General change detection algorithms require accurate registration. Otherwise, the change information is inaccurate. The TDSCC method is a feature-level or target-level change detection algorithm and it does not require registration operation. Therefore, it avoids errors in change information. The experimental data have confirmed the feasibility of the proposed algorithm.