粗糙地面上复杂目标的正向散射中心参数化建模研究

为了研究复杂目标与粗糙地面的复合电磁散射,提出了一种粗糙地面上复杂目标散射中心参数化建模的方法.首先对复杂目标进行高精度三维几何建模,同时对地面的三维几何模型进行数值模拟.然后直接从模型出发,采用射线分裂追踪技术对空间射线进行标记与归类,将复杂环境和目标的强散射源进行分离及定量表征.最后基于属性散射中心模型形式,正向确定散射中心模型的参数,构建粗糙地面上复杂目标散射中心参数化模型.文章给出了裸土上坦克的散射中心参数化模型,并将重构的合成孔径雷达（synthetic aperture radar,SAR）图像与可信的高频算法仿真SAR图像进行对比分析,重构结果与仿真结构具有较好的一致性,验证了对该类目标进行参数化建模的有效性,拓展了正向参数化建模目标适用范围.     

基于改进SBR技术的地面目标SAR图像快速生成技术

传统的弹跳射线(shooting and bouncing ray,SBR)技术主要适用于自由空间目标的电磁散射计算,当用于地面目标与背景复合场景的电磁散射计算和合成孔径雷达(synthetic aperture radar, SAR)图像建模与仿真时,在小擦地角下其计算误差显著增大。针对此问题,提出了一种基于“四路径”模型的改进SBR技术,其考虑了目标与邻域地面之间的多次反射效应以及目标与远区地面之间的多径散射效应对地面目标散射回波的影响。首先构建一个尺寸略大于目标包围盒的粗糙地面实体模型;然后结合“镜像法”改进射线追踪技术,分析电磁波在目标各部件间、目标同实体地面之间以及同虚拟半空间分界面之间的射线传播过程;最后结合高频渐近方法完成电磁散射计算。利用精确数值法计算结果对所提出技术进行了验证,并将其应用于地面目标的SAR图像高逼真度仿真,同实测SAR图像比对表明了所提技术具有良好的适用性。     

基于DEM数据的机载雷达地杂波仿真研究

基于合成孔径雷达(SAR)的工作过程,对舰船目标进行基于回波信号的SAR成像仿真。目标建模时采用小面单元模型,将建好的精细舰船3 D模型,根据 SAR系统参数和分辨率要求划分为小面单元,然后对面元进行散射截面的计算。在计算目标散射截面时,结合射线追踪法充分考虑了目标的一次散射,二次散射效应,使仿真图像更加符合真实的 SAR图像特征。根据面元散射截面和位置信息,结合雷达工作过程,生成回波。再运用 RD成像算法处理回波,得到最终的仿真图像。将实验仿真图像与真实 SAR 图像对比,验证文中仿真方法的合理性。     

高分辨率SAR图像桥梁目标仿真与特性分析

该文针对多径散射特征,给出了一种高分辨率SAR图像的仿真与特性分析方法,以桥梁目标为例开展了分析,该仿真方法从SAR工作原理出发,利用几何光学法计算和分析桥梁目标的单次散射情况和多径散射情况,并根据雷达工作原理和成像机理利用散射数据获得仿真SAR图像。文中选择了悉尼大桥为研究对象,利用分辨率为1 m的Terra-SAR数据开展了实验,仿真获得的SAR图像与实际SAR图像的主要散射特征一致,验证了方法的有效性;同时,根据仿真算法的分析过程,文中给出了悉尼大桥SAR图像主要散射特征的细节解释。实验表明,该方法能够有效获得目标的多径散射特征,同时能够有效辅助目标的SAR图像理解工作。      

分析圆柱油罐目标合成孔径雷达图像的新方法

该文提出了一种针对油罐目标的合成孔径雷达(SAR)图像的分析方法。该方法根据圆柱油罐具有圆型边缘和圆柱型外形的几何特点,利用物理光学法(Physical Optics,PO)和增量绕射长度(Incremental Length DiffractionCoefficients,ILDC)理论建立了油罐目标的散射模型,并基于该模型推导了圆柱油罐的散射中心分布特征,同时引入投影映射算法(Mapping and Projection Algorithm,MPA)建立了成像模型,有效利用了单一视角的散射数据进行SAR图像模拟,仿真结果与实际SAR图像散射特征吻合,验证了方法的有效性。同时,利用45组实测数据得到了SAR图像散射中心的特征,并根据建立的散射模型和成像模型,分析和总结了油罐目标SAR图像的主要散射特征,为SAR图像中基于油罐目标的识别领域提供了理论依据。     

基于电磁散射特性计算的目标SAR图像仿真

目标SAR图像仿真对SAR系统设计与验证、SAR图像几何纠正与地理编码、SAR图像解译和目标识别以及SAR成像处理等具有重要意义.SAR图像仿真总体分为基于信号的仿真和基于特征的仿真,不同应用决定了其相应的仿真方法也各不相同.面向SAR图像解译与目标识别应用,采用基于信号的仿真,研究实现了基于电磁散射特性计算的目标SAR图像仿真,给出了仿真信号的数学模型及仿真流程,并通过简单几何体以及实际目标的SAR图像仿真实验结果验证了其有效性.     

基于电磁散射模型的海上舰船双基SAR图像仿真

合成孔径雷达(synthetic aperture radar, SAR)在地海环境遥感和目标探测识别中发挥着重要作用。相对于单基SAR,双基SAR能够通过调整发射机和接收机的角度对场景进行多方位观测,因此开展海上舰船目标双基SAR图像仿真可为非合作目标的特征研究和探测识别提供方法和手段,在目标探测识别方面有着重要意义。为此,本文利用基于面元化的简化小斜率近似(facet-based simplified small slope approximation, FBS-SSA)方法与几何光学/物理光学(geometrical optics/physical optics, GO/PO)混合方法,提出了一种基于电磁散射模型的海上舰船目标复合场景双基SAR图像仿真方法,实现了不同双基角下的海上舰船SAR图像仿真,并分析了雷达飞行方向与舰船角度、发射机和接收机相对位置、极化、海况对雷达图像的影响。结果表明,双基角、极化、舰船朝向都会对双基SAR图像产生较大的影响,因此可以通过获取不同双基角度下的复合场景SAR图像特征从而可以更好地开展舰船识别。此外,SAR图像中的阴影特征也可作为舰船识别的辅助...     

典型地面车辆目标SAR图像仿真与评估

典型地面车辆目标SAR图像仿真对SAR图像解译和目标识别具有重要意义。在光学区目标雷达散射截面仿真基础上,研究实现了典型地面车辆目标SAR图像仿真技术,给出了坦克目标的SAR图像仿真结果。在此基础上,进一步研究了SAR图像仿真性能评估技术,在目视定性评估的基础上,引入了两个面向SAR图像解译与目标识别的SAR图像仿真性能定量评估指标。最后,通过目标仿真SAR图像与MSTAR实测SAR图像的比对,对仿真性能进行了定性、定量的分析与评估,验证了方法的有效性。     

典型几何基元的高频散射建模方法梳理

高分辨率SAR图像中蕴含目标与环境丰富的信息,但复杂的电磁散射机制使其难以直观解译,这一直是SAR图像解译的重要研究课题.该文简单梳理了典型几何基元的高频散射建模方法,以面散射、线散射和点散射为线索简要回顾了若干高频散射机制的研究发展过程,并给出几种典型几何基元的散射机制表达式及部分仿真结果,分析了典型散射机制表征面临...     

基于CSG模型的SAR城区回波模拟

对城区的SAR回波仿真是一个比较困难的问题.在城区场景的SAR图像中,地面一墙壁结构、屋顶、屋脊、屋檐、地面是SAR图像中重要的散射源.利用构造几何模型对城区建筑进行几何建模,将场景分解为大量三维立体网格,计算网格内主要散射源的散射系数.利用网格位置及其散射系数,在SAR空间几何关系下仿真了城区场景回波.成像结果表明该方法的有效性.     

多角度SAR图像非目标遮挡缺失信息重构

合成孔径雷达对地面目标侧视成像时,周围高大建筑或树木的阴影投射到目标上导致非目标遮挡,造成目标部分信息缺失,严重影响目标检测、识别以及跟踪的性能。SAR图像对角度敏感,相邻方位角图像之间相关性强,有助于缺失信息重构。本文首次将多角度SAR图像序列与张量分解结合,提出多角度SAR图像非目标遮挡缺失信息重构。首先,引入多路延时嵌入变换,将多角度SAR图像序列转换为块Hankel张量,以获取相邻方位角图像之间的潜在结构关系。然后,在嵌入的高维空间内进行Tucker分解,实现块Hankel张量缺失信息重构。最后对重构后的块Hankel张量进行多路延时嵌入逆变换实现目标缺失信息重构。MSTAR数据实验结果表明,该方法能实现非目标遮挡情况下车辆目标缺失信息重构,且优于经典算法HaLRTC。      

Space-time processing for multichannel synthetic aperture radar

Synthetic aperture radar (SAR) provides high-resolution images of a non-moving ground scene, but fails to indicate the presence and position of moving objects. As in airborne MTI (moving-target indication) systems the solution to this problem is to use an array of antennas or subapertures and several receiving channels (`MSAR', or multichannel SAR), and to apply multichannel clutter suppression. One of the most efficient methods is adaptive space-time processing (STAP), which can be simplified to frequency-dependent spatial processing in the Doppler domain. Some of these techniques applied to SAR are reviewed and illustrated with data gathered by the German experimental multichannel SAR system `AER-II'     

叶簇穿透合成孔径雷达增强成像方法

叶簇穿透合成孔径雷达(FOPEN SAR)为了探测树林中隐藏目标,通常工作在低信杂比(SCR)的环境中,目标检测难度极大。该文提出一种基于目标散射方位变化特性的增强成像方法,在保证高空间分辨率的同时,增强隐蔽目标图像SCR。对于感兴趣的车辆类目标,目标主侧面与地面形成二面角。当电磁波沿二面角法线方向入射时,回波能量最强。该方法能够获得该方位角对应的高分辨率图像,从而增强目标SCR,提高检测性能。实测机载FOPEN SAR数据验证了该方法的有效性。      

基于支持向量机的极化SAR图像分类

极化合成孔径雷达可以同时得到地面场景在不同极化组合下的雷达图像,极大地丰富了获取的地物目标信息量。针对极化SAR图像特征提取和目标分类的困难,由4种基本极化组成样本向量,运用基于统计学习理论的支持向量机以及不同的核函数设计分类器,提出了一种新的极化SAR图像分类算法,并对实测极化SAR数据进行分类实验。结果表明,将支持向量机分类器应用于极化SAR图像分类中是可行和有效的,并且通过选择适当的惩罚系数,可以进一步提高分类效果。     

单通道UWB SAR地面运动目标变化检测

工作在VHF/UHF波段的超宽带合成孔径雷达（UWBSAR）具有叶簇穿透和高分辨成像的能力。文中提出了一种适合单通道UWBSAR的地面运动目标检测方法——基于子孔径图像变化检测的运动目标检测算法。该算法利用UWBSAR的大波束角特点,在方位向选取较大时间间隔的两个子孔径生成子孔径图像,并根据运动目标在不同子孔径图像上聚焦位置的差异检测运动目标,估计运动目标速度和位置。文中给出了该方法的原理、参数选择、实现步骤,并基于UWBSAR实测数据验证了所提方法的有效性。     

Improving Coherence of Complex Image Pairs Obtained by Along-Track Bistatic SARs Using Range–Azimuth Prefiltering

An along-track interferometric synthetic aperture radar (SAR) can be used for ground moving target indication (GMTI) by comparing two SAR images obtained at different observation times. Different geometries of the two observations bring the decorrelation noise, which will degrade the detection performance. For bistatic SARs, the decorrelation theory is quite different from that for monostatic ones. This paper deals with the coherence between two complex SAR images formed by two along-track bistatic SARs with different baseline lengths. Using the single scattering model, the coherence between the two echoes collected by the two receivers is investigated, and the full-coherence conditions are derived. Then, a new method based on range-azimuth prefiltering is proposed to improve the coherence of complex image pairs. As the precise prefiltering is complicated, its three approximate implementations are given. The effects of prefiltering on SAR images are also analyzed. Finally, simulation results validate the effectiveness of the proposed method.     

Rectangular building extraction from stereoscopic airborne Radar images

From the recent availability of images recorded by synthetic aperture radar (SAR) airborne systems, automatic results of digital elevation models (DEMs) on urban structures have been published lately. This paper deals with automatic extraction of three-dimensional (3-D) buildings from stereoscopic high-resolution images recorded by the SAR airborne RAMSES sensor from the French Aerospace Research Center (ONERA). On these images, roofs are not very textured whereas typical strong L-shaped echoes are visible. These returns generally result from dihedral corners between ground and structures. They provide a part of the building footprints and the ground altitude, but not the building heights. Thus, we present an adapted processing scheme in two steps. First is stereoscopic structure extraction from L-shaped echoes. Buildings are detected on each image using the Hough transform. Then they are recognized during a stereoscopic refinement stage based on a criterion optimization. Second, is height measurement. As most of previous extracted footprints indicate the ground altitude, building heights are found by monoscopic and stereoscopic measures. Between structures, ground altitudes are obtained by a dense matching process. Experiments are performed on images representing an industrial area. Results are compared with a ground truth. Advantages and limitations of the method are brought out.     

用于地面目标微波特性测量的飞艇SAR系统

针对地面目标微波散射特性的高分辨率成像测量需求,该文给出了一种基于飞艇平台的合成孔径雷达(SAR)测量系统。介绍了飞艇SAR系统的构成和功能,详细探讨了合成孔径雷达的技术性能,提出了针对飞艇慢速飞行平台所采取的结合IMU,DGPS和自聚焦的成像和运动补偿方案。通过实际飞行试验,验证了飞艇SAR对目标高分辨率成像测量的可行性。     

Scattering-Based Tomography for HRR and SAR Prediction

This paper introduces a method for predicting HRR radar signatures and SAR images by creating a parametric three-dimensional scattering model from existing measured or model-based HRR signatures and/or SAR images. The method identifies potential three-dimensional persistent scatterers and estimates their scattering patterns. The results are parametric HRR signature and SAR image reconstruction functions of range, azimuth, and elevation.The modeling is accomplished through a scattering-based tomography technique. This technique localizes potential scatterers by using a filtered back-projection algorithm for the inverse radon transform. Once found, potential scatterers may then have their two-dimensional (azimuth and elevation) scattering patterns parameterized through the use of a truncated spherical harmonic series.Results using the reconstructions from HRR data are presented. A M109 model is reconstructed based on HRR signatures. The model allows us to predict what the vehicle would look like from any arbitrary orientation using SAR. Finally an M548 vehicle is modeled using 26 measured HRR signatures. The model is shown to be better than the synthetic model data. Additionally we show that the new model results can be combined with the synthetic data to provide a better target model for signature matching.     

SAR image despeckling using heavy-tailed Burr distribution

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.