Computer Simulation of Synthetic Aperture Radar Images of Three-Dimensional Objects

A digital simulation of coherent synthetic aperture radar (SAR) images of three-dimensional objects is described. The simulation is intended to produce representative SAR images that would be suitable for image analysis and pattern recognition studies. The procedure involves a modeling of the object using a combination of three-dimensional quadratic shapes yielding a smooth surface representation. The radar images of these models are then computed using physical optics scattering theory. Finite resolution both in range and cross-range direction is incorporated via a theoretical analysis which results in a simple Fourier transform representation of an equivalent "offset" window filter. Examples of the computer simulation for both infinite resolution and blurred or finite resolution are given for a KC-135 aircraft model.     

Polynomial Representation of NURBS and Its Application to High Frequency Scattering Prediction

This article presents a method that uses physical optics (PO) techniques to compute the monostatic radar cross section (RCS) of electrically large conducting objects modeled by non-uniform rational B-spline (NURBS) surfaces. At the beginning, a new algorithm to convert recursive B-spline basis function into piecewise polynomials in power form is presented. Then, algorithm computes the polynomial representation of B-spline basis functions and NURBS surface geometric parameters are obtained. The PO integral over NURBS surfaces of an electrically large conducting object is used to predict the object's RCS. The NURBS surface is divided into small piecewise polynomial parametric patches by isoparametric curves, and the PO integral expression over the parametric domain of each polynomial parametric patch is reduced to an analytical expression which permits an accurate and effective computation of the PO integral by using a modified Ludwig's algorithm. The RCS of the object can be obtained by adding up the PO integral contribution of each polynomial parametric patch. The effectiveness of this method is verified by numerical examples.     

基于红外视觉/激光雷达融合的目标识别与定位方法

近年来,基于可见光图像的目标识别在无人车感知领域得到了广泛应用.然而,可见光图像目标识别无法应用于弱光和黑暗环境.针对于此,提出了一种基于红外视觉/激光雷达融合的目标识别与定位算法.首先,通过基于颜色迁移的数据增强训练方法,提高了红外目标识别算法的泛化性能.继而,提出了一种基于激光雷达修正的单目深度估计方法,通过视觉图...     

飞船返回舱红外跟踪

提出一种联合大底检测的改进的Mean-Shift（均值移位）算法,将其应用于返回舱红外跟踪。新算法采用非均匀量化加权直方图构造目标特征矢量,以提高目标描述的准确性。为避免跟踪到大底上去,使用＂滑窗式＂大底检测算法实时修正Mean-Shift跟踪坐标。通过对飞船返回实况判读验证,提出的新算法处理速度达到20帧/s以上,并且能够可靠和准确地跟踪返回舱。     

Spatio-temporal pattern recognition using hidden Markov models

A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7%, are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques     

逆向工程中曲面重构算法研究与实现

给出了基于坐标测量机测得的海量数据重新构造样板物体的数字化表面模型的算法流程。该算法首先利用部分测量数据构造张量积的B样条网格曲面,通过确定适当的检查点,并计算检查点到所构造的B样条网格曲面间的距离,从而控制重构曲面的构造精度。讨论了与文中算法相关的空间点到NURBS曲面间的距离计算等问题。给出了1个工程应用实例,表明该算法稳定可靠、效率较高。     

An efficient 3-D near-field ISAR algorithm

This paper presents a new three-dimensional (3-D) near-field inverse synthetic aperture radar (ISAR) imaging technique. A 3-D ISAR image can be obtained by processing coherently the backscattered fields as a function of the frequency and two rotation angles about axes which are mutually orthogonal. Most of the existing ISAR algorithms are based on the Fourier transform and as such can tolerate only small amounts of wavefront curvature. Wavefront curvature must be taken into account when imaging an object in the near-field. Near-field ISAR imaging of large objects using a direct Fourier inversion may result in images which are increasingly unfocused at points which are more distant from the center of rotation. An algorithm based on an azimuth convolution between a near-field focusing function and the frequency domain backscattered fields is discussed. This convolution is efficiently implemented by using fast Fourier transform (FFT) techniques. Furthermore, in order to further alleviate the computational load of the algorithm, the discrete Fourier transform (DFT) of the focusing function is evaluated by means of the stationary phase method. Experimental results show that this technique is precise and virtually impulse invariant     

3-D E-CSAR imaging of a T-72 tank and synthesis of its SAR reconstructions

The results of three-dimensional (3-D) imaging of a T-72 tank using its angular azimuthal (turntable) and linear elevation synthetic aperture data at X band are presented. This is achieved using an accurate and computationally efficient wavefront (Fourier-based) reconstruction algorithm for elevation and circular (E-CSAR) data. The E-CSAR 3-D images are then used to synthesize 2-D spotlight and stripmap slant plane synthetic aperture radar (SAR) images of the target at a desired range and squint angle. For this purpose, a procedure is introduced that incorporates the spatially varying azimuthal and elevation Doppler signatures of individual reflectors on the target as well as the mean range, azimuth, and elevation of the flight path. Results using the E-CSAR images of the T-72 tank are provided.     

一种三维物体的视觉识别方法研究

研究了一种利用二维图像识别三维物体的方法。通过颜色特征提取物体的有效区域．使用链码描述了区域中三维物体的形状特征，并形成物体的形状特征向量。将该特征向量作为所设计的BP神经网络的输入进行物体匹配识别，网络输出为属于某种物体的隶属度。通过在各种光照环境下不同三维物体的二维彩色图像进行实验，证明了所提出方法的有效性。     

基于视频图像序列的3-D场景生成方法

平面序列图像的立体化表述和任意视点成生是实现３－Ｄ场景的关键技术。利用便携式摄像机沿着不同运动轨迹,连续对目标物体进行拍摄,可获得不同层次的包含有双目视差深度的平面视点序列图像。从获取的序列图像中选择不同的视点,即选择参考帧,利用与参考帧相邻的帧图像,估计参考帧位置的深度信息。采用自适应匹配算法,将各种层次的深度信息分配给图像的不同区域,形成具有不同深度层次的图像深度表示图,建立３－Ｄ观察场景。最     

一种超高分辨率机载聚束SAR两维自聚焦算法

受运动参数测量误差和大气扰动等因素影响,合成孔径雷达(SAR)图像通常会发生散焦,利用自聚焦对散焦的SAR图像进行后处理是一种有效的重聚焦手段。传统的自聚焦算法都只是针对方位一维相位误差的估计和补偿。随着成像分辨率的提高,自聚焦时残留距离徙动的校正成为SAR成像面临的一个新挑战。本文推导得到了极坐标格式算法处理后残留距离徙动和方位相位误差的解析表达式,分析了两者之间的内在关系,并利用该关系,提出了一种能够同时进行残留距离徙动和方位相位误差补偿的两维自聚焦算法。实测数据处理结果表明,在残留距离徙动效应不可忽略的条件下,该方法能够极大地改善原有自聚焦算法的聚焦性能。     

基于改进HOG特征的空间非合作目标检测

传统的非合作目标检测方法大都基于一定的匹配模板,这不仅需要预先指定先验信息,进而设计合适的检测模板,而且同一模板只能对具有相似形状的目标进行检测,不易直接用于检测形状未知的非合作目标。为降低检测过程中对目标形状等先验信息的要求,借鉴基于规范化梯度的物体区域估计方法,提出一种基于改进方向梯度直方图特征的目标检测方法,首先构建包含有自然图像和目标图像的训练数据集;然后提取标记区域的改进方向梯度直方图特征,以更好地保持局部特征的结构性,并根据级联支持向量机训练模型,从数据集中自动学习目标物体的判别特征;最后,将训练后的模型用于检测测试集图像中的目标。实验结果表明,算法在由4953幅和100幅图像构成的测试集中分别取得94.5%和94.2%的检测率,平均每幅图像的检测时间约为0.031 s,具有较低的时间开销,且对目标的旋转及光照变化具有一定的鲁棒性。     

SAR image formation via semiparametric spectral estimation

A new algorithm, referred to as the SPAR (Semiparametric) algorithm, is presented herein for target feature extraction and complex image formation via synthetic aperture radar (SAR). The algorithm is based on a flexible data model that models each target scatterer as a two-dimensional (2-D) complex sinusoid with arbitrary unknown amplitude and constant phase in cross-range and with constant amplitude and phase in range. By attempting to deal with one corner reflector, such as one dihedral or trihedral, at a time, the algorithm can be used to effectively mitigate the artifacts in the SAR images due to the flexible data model. Another advantage of SPAR is that it can be used to obtain initial conditions needed by other parametric target feature extraction methods to reduce the total amount of computations needed. Both numerical and experimental examples are provided to demonstrate the performance of the proposed algorithm     

A man-made object detection algorithm based on contour complexity evaluation

Man-made object detection is of great significance in both military and civil areas, such as search-and-rescue missions at sea, traffic signs recognition during visual navigation, and targets location in a military strike. Contours of man-made objects usually consist of straight lines, corner points, and simple curves. Motivated by this observation, a man-made object detection method is proposed based on complexity evaluation of object contours. After salient contours which keep the crucial information of objects are accurately extracted using an improved mean-shift clustering algorithm, a novel approach is presented to evaluate the complexity of contours. By comparing the entropy values of contours before/after sampling and linear interpolation, it is easy to distinguish between man-made objects and natural ones according to the complexity of their contours.Experimental results show that the presented method can effectively detect man-made objects when compared to the existing ones.     

高轨目标掩星成像相位恢复技术研究

高轨目标掩星成像通过观测高轨卫星对恒星的遮掩过程,实现对高轨目标的成像.该方法采用望远镜线性阵列观测高轨目标遮掩恒星过程中恒星亮度变换,获得高轨目标在地面的投影,通过相位恢复方法可计算得到卫星轮廓图样,具有成像分辨力高,设备制造难度小等优点,在高轨目标成像领域具有较好应用前景.本文分析利用掩星成像方法对高轨目标成像的可行性,重点对掩星成像过程的相位恢复方法进行改进,根据掩星成像特点对输入输出算法进行优化,仿真结果表明：改进后的相位恢复方法可显著提高图像恢复质量和掩星成像方法的空间分辨力.     

气动变量参数化的压气机转子三维数值优化

采用"试验设计+响应面模型+遗传算法"的优化设计体系,结合压气机设计常用的叶片造型程序和流场模拟软件,搭建了轴流压气机叶片三维优化设计平台,并对某涡喷发动机加零级压气机的零级转子进行了优化.优化目标为极大化转子的设计点绝热效率.约束条件为流量、增压比基本不变.分别以相对气流角和气流脱轨角作为优化自变量,进行了两个算例的优化.即为与现代设计系统相接轨,不同于叶片几何参数优化,取设计中具有物理含义的气动参数作为优化自变量.优化后的绝热效率分别提高了0.82和0.73个百分点.      

RESEARCH AND REALIZATION OF MUTUAL TRIMMING OF NURBS

Sincetheearly199Os,someCAD/CAMsystems,suchasUG1,SuperMan,havebeenbasedontrimmedsurfacesinsurfacemedelling.Fortheflexibilityandconvenienceofsurfacemodelling,mutualtrimmingbetweentwosurfacesisoftendesired,soitisnecessarytostudythetechniqueofmutualtrimmingofsurfaces.Duringrecentyears,therehadbeensomepapersontrimmedsurface[l~4JamongwhichRef.[2jpresentedacomparativelycomprehensiveandsystematicintroductiontotrimmingofNURBSsurface.Nevertheless,accordingtoRef.L2],retrimmingofatrimmedsurfaceisn…     

Object-based SAR image compression using vector quantization

A simple and elegant algorithm is presented to encode images with rich content, which allows easy access to various objects. An object-plane-based encoding method for compression of synthetic aperture radar (SAR) imagery is developed, with different object planes for target classes and background. A variable-rate residual vector quantization (VQ) algorithm is developed to encode the background information. This algorithm is very powerful as indicated by the experimental results. The proposed coding scheme allows compression matched to the final application of the images, which in this case is target recognition and classification.     

Application of velocity filtering to optical-flow passive ranging

The performance of the velocity filtering method as applied to optical-flow passive ranging under real-world conditions is evaluated. The theory of the 3-D Fourier transform as applied to constant-speed moving points is reviewed, and the space-domain shift-and-add algorithm is derived from the general 3-D matched filtering formulation. The constant-speed algorithm is then modified to fit the actual speed encountered in the optical flow application, and the passband of that filter is found in terms of depth (sensor/object distance) so as to cover any given range of depths. Two algorithmic solutions for the problems associated with pixel interpolation and object expansion are developed, and experimental results are presented