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.     

复杂电磁散射目标的计算机建模

雷达截面的理论分析与预估是飞机隐身技术研究中的重要环节。在计算复杂目标的雷达散射截面时,目标几何建模是一个关键问题,建模方式的选择直接影响计算结果的精确度和整体效率。对目标几何建模方法及其特点进行了研究,采用两种不同的面元法建立目标模型,并且利用后向面判别和深度缓冲器算法的结合来处理不同面元间的遮挡问题。     

短切碳纤维电磁散射特性仿真研究

研究单根碳纤维的电磁散射特性,如何改善碳纤维的吸波性能,是碳纤维复合吸波材料的难点问题.为上述问题,提出用圆柱体模型模拟碳纤维,根据三维电磁场有限元(FEM)算法,当平面波激励时,采用四面体棱边元网格划分及辐射边界条件以及矩阵方程组的自适应迭代算法,在Ansoft HFSS仿真平台上模拟了碳纤维的电场散射特性,获得了单根碳纤维的电场散射图.结果表明,在合适的长径比范围内,碳纤维与人射波产生强烈的谐振,碳纤维的电导率、轴向和入射波频率对其电磁波散射均有重要影响.仿真结果对设计具有强吸波性能的短切碳纤维复合吸波材料具有参考价值.     

复杂介质球对平面波电磁散射的解析解

利用球矢量波函数对复杂介质球电磁散射的解析解开展了理论研究,并给出了数值计算结果。方法是从无源麦克斯韦方程组,结合媒介的本征结构推导出含参数的矩阵方程,再由矩阵方程的非零解的存在条件解出方程中的参数,然后将解出的参数反代入矩阵方程得到矩阵方程的非零解,进而推出介质球中的电磁场的解析表达式,再利用在复杂介质球表面电场、磁场所满足的边界条件求得散射场。     

频率步进雷达多目标ISAR成像方法

针对频率步进雷达同一波束内多个运动目标在径向上重叠 而无法分辨时的ISAR成像问题,提出了一种基于调频傅里叶变换的ISAR多目标成像新方法。 在构建频率步进雷达多目标回波信号模型的基础上,采用调频傅里叶变换精确估计各个目标 的速度参数,结合 Clean方法实现对多目标回波信号的分离,完成多目标ISAR成像。仿真实 验结果进一步验证了文章所采用方法的有效性。     

宽带雷达动态目标建模及仿真研究

对复杂动态目标进行回波建模是研究目标散射特性，实现雷达跟踪识别动态目标的重要基础。该文利用宽带雷达的高分辨特性，分析了宽带信号照射到运动点目标上的回波信号形式，根据高频区目标总的电磁散射是多散射中心的合成的原理，得出了宽带雷达动态复杂目标的回波模型，并针对冲击雷达信号运用该模型进行回波仿真，获得给定目标在平动及转动情况下的回波仿真信号。仿真结果较好地反映出目标特征，为进一步进行雷达目标的识别提供了理论分析依据。     

海天背景下三维目标红外成像仿真方法研究

通过建立的目标三维模型和场景数学模型,利用光线跟踪技术与图像融合算法,实现了海天背景下指定场景信息的目标红外成像仿真.首先,介绍仿真的主要流程,然后对建立三维目标信息、实现光线跟踪算法、目标红外图像与背景红外图像融合三部分分别进行了讨论,最后给出了实验生成的海天背景下指定位置的三维目标的仿真红外图像.     

介质粗糙面与埋藏导体目标的电磁散射特性研究

本文将三维矢量有限元-边界元方法(Finite Element Method-Boundary Integral Method)与快速多极子(Fast Multi-pole Method,FMM)方法结合,研究了二维介质粗糙面下方埋藏导体目标的复合电磁散射特性。推导了复合模型电磁散射的FEM-BIM公式,针对FEM-BIM矩阵方程组的特点,采用混合迭代求解器求解FEM-BIM矩阵方程组,并采用FMM加速迭代求解过程,与矩量法(Method of Moments,MoM)和传统FEM-BIM方法的比对结果验证了本文算法的高效性和精确性。最后,结合Monte-Carlo方法,研究了地面下方炸弹型目标的复合电磁散射特性,分析了土壤湿度、埋地深度和地面粗糙度等参数变化对复合模型双站散射系数的影响。     

共形PML下三维电磁散射矢量有限元计算

共形完全匹配层吸收边界能满足复杂构型散射体的几何外形要求,又具有良好的电磁波吸收特性,大幅度减少了散射区网格的数量,达到节省计算机存储量的目的。在ANSYS采用的六面体矢量单元的基础上,对矢量基函数作了一些增强正交性的修改,使其同时具备采样点正交性和积分正交性,得到了一种新的正交增强六面体矢量单元;这种新单元能增强系统矩阵的对角占优特性,加快系统方程组的求解速度。数值算例表明,在共形完全匹配层条件下,采用新的正交增强矢量单元进行三维电磁散射计算既能减少计算存储量,又能提高计算速度和精度,具有工程应用价值。     

基于多次散射目标模型的雷达成像研究

合成孔径雷达（Synthetic Aperture Radar, SAR）作为一种主动微波传感器,被广泛应用于遥感对地观测。然而,传统上的雷达散射模型是基于几何光学近似的,忽略了地物目标之间的相互作用,即多次散射效应。为了充分挖掘和利用毫米波雷达影像中目标电磁散射特性信息,亟需对典型目标的多次散射效应进行建模仿真和验证。基于矩量法（Method of Moments, MoM）分析了球体、二面角等目标表面等效电流分布与分段成像结果的对应关系,并利用后向投影（Back-Projection, BP）算法进行电磁仿真成像,总结了单/双基地雷达模式对成像中散射机制的影响。结果表明：目标表面等效电流分布以及分段等效合成孔径张角会随入射角度发生改变,其中分段等效合成孔径张角会影响方位向分辨率;双基地雷达成像结果中包含更丰富的电磁散射信息。本研究可为SAR系统设计和验证、典型目标回波特性数据收集以及如何基于高解析度SAR影像进行目标识别等研究提供借鉴与参考。     

基于雷达复杂1维距离数据的3维重建算法

基于刚体目标3维运动过程中的几何不变性,可以利用目标上多个散射点在单天线雷达1维距离像序列中的1维距离数据,重建出目标未知的3维结构和运动路径。针对此1维到3维的几何重构问题,提出了一种雷达刚体目标结构和运动的3维重建算法,该算法可利用散射点复杂的1维距离数据进行重建,并且采用非线性优化技术实现了对目标重建参数的捆绑调整(bundle adjustment)。另外,该算法中引入了目标的平移模型,使目标的平移参数能够与旋转参数一并求解,从而避免了距离对准操作的误差对重建精度的影响。仿真实验结果表明,由于重建数据中散射点的数量以及目标3维运动的丰富性得到了显著提高,尤其是最优化技术在算法中的成功应用,重建算法的鲁棒性得到了有效增强。     

基于二维激光雷达的自动室内三维重建系统

设计了一个基于二维激光雷达的自动室内三维重建系统.系统的硬件由一套自行设计的基于2D激光雷达的三维扫描系统和一台电脑构成.介绍了系统的软件模块,提出了结合最近点迭代(ICP)和通用多边形裁剪(GPC)的3D平面场景合成方法.ICP能够获得不同采集位置之间的位置变化,以此能将各个不同位置获得的3D场景转换到同一坐标系下.场景合成时的碎平面问题通过GPC方法来解决.实验结果表明:该系统成本低,精度高,能稳定可靠地实现室内场景的自动三维重建.     

3D-CDRNet: Retrieval-based dense point cloud reconstruction from a single image under complex background

3D reconstruction technique based on deep learning is gaining increasing attention from researchers. The majority of current 3D reconstruction techniques require a simple background, which limit their applications on complex background image. Extracting point cloud features comprehensively is also extremely difficult. This paper design a novel 3D reconstruction network to overcome these limitations. Firstly, we get the image and the retrieved point cloud that is the most similar to the input image. Secondly, to learn the features of the retrieved point cloud, the network encodes and decodes the single image and the retrieved point cloud to generate sparse point cloud. Finally, the proposed dense module densifies the sparse point cloud into the dense point cloud. We use single image of complex background and public dataset to evaluate our network. The reconstruction results indicate that the network surpasses previous reconstruction networks.     

Automated reconstruction of 3D input data for noise simulation

Noise is one of the main problems in urban areas. To monitor and manage noise problems, governmental organisations at all levels are obliged to regularly carry out noise studies. The simulation of noise is an important part of these studies. Currently, different organisations collect their own 3D input data as required in noise simulation in a semi-automated way, even if areas overlap. This is not efficient, but also differences in input data may lead to differences in the results of noise simulation which has a negative impact on the reliability of noise studies. To address this problem, this paper presents a methodology to automatically generate 3D input data as required in noise simulations (i.e. buildings, terrain, land coverage, bridges and noise barriers) from current 2D topographic data and point clouds. The generated data can directly be used in existing noise simulation software. A test with the generated data shows that the results of noise simulation obtained from our generated data are comparable to results obtained in a current noise study from practice. Automatically generated input data for noise simulation, as achieved in this paper, can be considered as a major step in noise studies. It does not only significantly improve the efficiency of noise studies, thus reducing their costs, but also assures consistency between different studies and therefore it improves the reliability and reproducibility. In addition, the availability of countrywide, standardised input data can help to advance noise simulation methods since the calculation method can be adopted to improved ways of 3D data acquisition and reconstruction.     

Closed‐form method for the reconstruction of 2‐D objects: Preliminary experimental results

An analytical approach to inverse electromagnetic scattering is tested on 2‐D experimental data. The closed‐form singular value decomposition of the scattering integral operator is the basis for determining the radiating components of the equivalent source density. This equivalent source is used to reconstruct the features of a scatterer. Reconstructions performed on different scattering data show the capabilities of the method and, thanks to the closed‐form solution, results are available after a very short time of computation. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

利用序列ISAR图像获取空间目标3-D信息的方法

如何从空间目标序列性二维(2-D,Two-Dimentional)逆合成孔径雷达(ISAR,Inverse Synthetic Aperture Radar)成像获取目标的三维(3-D)信息,是目标特征自动识别(ATR,Automatic Target Recognition)技术的重要研究课题。利用双向射线跟踪(BART,Bidirectional Analytic Ray Tracing)方法,计算连续多角度观测条件下空间目标的电磁散射数据,并由此获取空间目标的ISAR序列2-D图像。再利用KLT(Kanade-Lucas-Tomasi)特征跟踪算法,跟踪提取2\|D序列ISAR图像中的特征点(强散射点),获得其2-D坐标。然后,基于正交因式分解法(OFM,Orthographic Factorization Method),计算强散射点的3\|D坐标,获取空间目标的3-D信息。通过简单六棱柱模型,验证重构算法的精度;并以ENVISAT卫星模型为例,给出强散射点的3-D重构结果。结果表明,本文对空间目标3\|D信息获取方法能有效地从ISAR序列2-D图像中重构目标的三维信息。     

Constrained 3D shape reconstruction using a combination of surface fitting and registration

We investigate 3D shape reconstruction from measurement data in the presence of constraints. The constraints may fix the surface type or set geometric relations between parts of an object's surface, such as orthogonality, parallelity and others. It is proposed to use a combination of surface fitting and registration within the geometric optimization framework of squared distance minimization (SDM). In this way, we obtain a quasi-Newton like optimization algorithm, which in each iteration simultaneously registers the data set with a rigid motion to the fitting surface and adapts the shape of the fitting surface. We present examples to show the applicability of our method to constrained 3D shape fitting for reverse engineering of CAD models and to high accuracy fitting with kinematic surfaces, which include surfaces of revolution (reconstructed from fragments of archeological pottery) and spiral surfaces, which are fitted to 3D measurement data of shells. Our optimization algorithm can combine registration of multiple scans of an object and model fitting into a single optimization process which is shown to be superior to the traditional procedure, which first registers the data and then fits a model to it.     

3D fingerprint reconstruction system using feature correspondences and prior estimated finger model

The paper studies a 3D fingerprint reconstruction technique based on multi-view touchless fingerprint images. This technique offers a solution for 3D fingerprint image generation and application when only multi-view 2D images are available. However, the difficulties and stresses of 3D fingerprint reconstruction are the establishment of feature correspondences based on 2D touchless fingerprint images and the estimation of the finger shape model. In this paper, several popular used features, such as scale invariant feature transformation (SIFT) feature, ridge feature and minutiae, are employed for correspondences establishment. To extract these fingerprint features accurately, an improved fingerprint enhancement method has been proposed by polishing orientation and ridge frequency maps according to the characteristics of 2D touchless fingerprint images. Therefore, correspondences can be established by adopting hierarchical fingerprint matching approaches. Through an analysis of 440 3D point cloud finger data (220 fingers, 2 pictures each) collected by a 3D scanning technique, i.e., the structured light illumination (SLI) method, the finger shape model is estimated. It is found that the binary quadratic function is more suitable for the finger shape model than the other mixed model tested in this paper. In our experiments, the reconstruction accuracy is illustrated by constructing a cylinder. Furthermore, results obtained from different fingerprint feature correspondences are analyzed and compared to show which features are more suitable for 3D fingerprint images generation.     

ST-Rec3D：基于结构和目标感知的三维重建

基于视图的三维重建旨在从二维图像恢复出其对应的三维形状。现有方法主要通过编码器-解码器结构,结合二元交叉熵函数及其变形,完成三维重建,取得较好的重建结果。然而,编码器在编码过程中缺乏对输入视图的结构感知能力,造成重建的三维模型几何细节不准确;以二元交叉熵函数为主的损失函数在体素分布不均衡的情况下,目标感知能力较差,导致其重建结果存在断裂、缺失等不完整性问题。针对此类问题,提出了一种具有结构和目标感知能力的三维重建网络(ST-Rec3D),以单视图或多视图为输入,由粗到细地重建出三维模型;结合注意力机制提出了一种具有空间结构感知能力的编码器,即结构编码器,以充分捕捉输入视图中的空间结构信息,有效感知重建物体的几何细节;将IoU损失引入到三维体素模型重建中,在体素分布不均衡的情况下,精准感知目标物体,确保重建物体的完整性和准确性。在ShapeNet和Pix3D数据集上的对比结果表明,ST-Rec3D在单视图和多视图上重建的三维模型的完整性和准确性均优于当前方法。