【作者】 唐宁；

【导师】 黎湘；

【作者基本信息】 国防科学技术大学 ， 信息与通信工程， 2012， 博士

【摘要】 逆合成孔径雷达（Inverse Synthetic Aperture Radar，ISAR）作为一种高分辨二维成像设备，能全天候全天时的获取反映目标大小形状结构及姿态等细节信息的二维高分辨雷达图像，可以为目标分类与识别提供丰富的特征信息基于ISAR像的目标识别技术在现代军事应用中发挥着非常重要的作用，受到国内外的热切关注本论文以空中目标识别为背景，针对空中目标ISAR像识别存在的问题，在对空中目标进行几何建模和仿真成像的基础上，对ISAR像预处理技术和空中目标ISAR像的特征提取算法等内容进行了较为深入的研究本文的主要内容概括如下：第一章阐述了论文的研究背景及其意义，较详尽地介绍了ISAR目标识别技术的发展概况，然后针对空中目标的气动布局，对飞机部件的散射特性和ISAR识别中的关键问题进行了分析，最后归纳和总结了论文的主要工作第二章研究了空中目标ISAR像的建模仿真和图像预处理技术首先，介绍了ISAR成像的基本原理，探讨了基于Mu1tigen Creator软件的Open Flight模型建模技术，并采用RadBase产生雷达回波，利用Matlab完成目标ISAR成像仿真；然后，分析了目标ISAR像的固有特性以及影响ISAR像的主要因素，并以F14和F18飞机为例讨论了不同姿态下ISAR像的相关性；最后，针对ISAR像的斑点噪声和横条纹干扰特性，提出了相应的干扰抑制算法，并对图像进行归一化处理，提高图像稳定性以利于后续的特征提取与识别第三章研究了ISAR像方位估计及其仿射不变特征提取算法首先，为了提高模板匹配效率，引入基于目标主轴和基于最小面积外接矩形的两种方位估计思想，在ISAR像强散射中心视角相关性分析基础上，提出了一种基于ISAR像方位估计的相关匹配算法，以方位角估计值为索引缩小模板搜索范围，提高了ISAR像模板匹配的效率；其次，针对ISAR像的平移姿态和尺度不一致性问题，获取了ISAR目标特征点及其仿射不变特征，并存入几何散列表中，然后采用几何散列法实现了目标的匹配识别，该算法能够有效区分不同结构不同视角和尺度目标，并具备较强的局部识别能力第四章研究了基于非负矩阵分解的ISAR像统计特征提取算法类比于人类视觉认知机制，以非负矩阵分解（NMF）为数学工具获取ISAR像的统计分类特征向量，提出了基于优化判别非负矩阵分解和基于子类判别非负矩阵分解的ISAR目标特征提取算法首先，针对ISAR目标的孤立稀疏强散射中心分布特性，分别采用局部非负矩阵分解（LNMF）和非负稀疏编码（NNSC）获取ISAR目标的局部性和稀疏性特征空间，然后引入线性判别思想，对特征空间特征基进行优化精选，提出了基于优化判别局部非负矩阵分解（ODLNMF）和优化判别非负稀疏编码（ODNNSC）的ISAR目标特征提取算法，从而通过优化特征基的鉴别性和鲁棒性提高ISAR目标分类识别性能；其次，针对ISAR像的多模特性，引入聚类判别分析（CDA）思想，在LNMF和NNSC的目标函数上加入子类判别限制，提出了基于子类判别局部非负矩阵分解（SDLNMF）和子类判别非负稀疏编码（SDNNSC）的ISAR目标特征提取算法，并利用Expectation-Maximization （EM）方法推导了各自的特征基和特征矢量的迭代规则实验证明，聚类判别分析思想的引入提高了目标识别的有效性和稳健性第五章研究了基于正交邻域子类判别投影（ONSDP）的流形特征提取算法首先，为了提取ISAR高维数据集中的低维流形特征，提出了正交邻域子类判别投影流形学习算法该算法融合了数据局部拓扑保持和子类判别降维思想，通过引入正交约束，使得获取的投影特征保持了原数据的欧式空间结构，有效地解决了ISAR像多模数据的分类问题；另一方面，为了更有效的利用ISAR非线性结构，通过在ONSDP算法加入核函数，提出了核正交邻域子类判别投影（KONSDP）算法，使用径向基核函数代替内积，隐式的将原始的非线性数据通过非线性映射到高维线性可分空间，给出了其推导过程及实验验证；最后，将ISAR像视为二阶张量，通过CDA和ONSDP的张量化发展，提出了张量子类判别分析（TSDA）和张量正交邻域子类判别投影（TONSDP）算法，并给出了推导过程TSDA和TONSDP保留了图像自身行与列之间的结构信息，同时加入了子类判别信息，最终将最优张量子空间求解归结为广义特征向量求解问题论文通过实验验证了所提算法在提高目标识别率方面的优势第六章总结全文，并指出了下一步需要开展的工作更多还原

【Abstract】 Inverse Synthetic Aperture Radar (ISAR) can obtain two-dimensional highresolution images, with detailed information, such as scale, shape, structure and gesture,which afford abundant features for classification and recognition of targets, in allweather, day or night. Therefore, the technique of automatic target recognition (ATR)using ISAR images is playing a very important role in modern military applications andis always being paid attention to. In order to resolve the problems of classifying airtargets using ISAR images, several special researches are made on the basis ofgeometric modeling and imaging simulation in this dissertation, including ISAR imagespre-processing and feature extraction algorithms of ISAR images.In chapter1, the research background and significance is introduced, and thedevelopment of ATR techniques using ISAR images is reviewed. Then aerodynamiclayout of air targets, scattering properties of aircraft components and the key in ATR ofISAR targets are analyzed in detail, followed by the introduction of main content in thisdissertation.In chapter2, modeling and simulation of ISAR targets, image pre-processingtechnology are inverstigated. At first, the principles of ISAR imaging, Open Flightmodeling with Mu1tigen Creator, simulation of ISAR imaging using Matlab with radarwave generated by RadBase are introduced; then, the inherent characteristics and mainfactors affecting ISAR images are analyzed, followed by correlation stduy betweenISAR images using F14and F18aircrafts as examples; finally, suppression of thespeckle noise and interferential stripes，images normalization are proposed to enhancethe stability of images，which is propitious to the latter feature extraction andrecognition.In chapter3, algorithms for aspect estimation and affine invariant featureextraction are presented. First of all, in order to improve the efficiency of templatematching, we combine the thoughts of target’s principal axis extraction and minimumenclosed rectangle acquirement, and propose a correlation matching method based onaspect estimation, which narrows the scope of template search by aspect estimation andimprove the efficiency of ISAR template matching; secondly, aimed at resolving theproblems of translation, gesture and scale inconsistencies, we extract key points ofISAR images, constructs affine-invariance features which are then saved in the hashtables, and uses geometric hash as the final classifier. Simulations show the presentedalgorithm not only effectively distinguishes targets with different structures, imagingperspective and scale, but also performs excellently in local recognition.In chapter4, algorithms for statistical feature extraction of ISAR images based onnon-negative matrix factorization (NMF) are proposed. Introducing visual perceptionconcept to classification, we adopt NMF as a mathematical tool to obtain the statistical feature vector of an ISAR image, and propose approaches of feature extraction based onoptimized discriminant non-negative matrix factorization and subclass discriminantnon-negative matrix factorization. At first, we construct a feature space based on localnon-negative matrix factorization (LNMF) and non-negative sparse coding (NNSC),which is convenient for local and sparse feature extraction, respectively, then optimizethe feature space by screening the feature base to enhance the classification performance,and finally present algorithms for ISAR target recognition based on optimized discri-minant local non-negative matrix factorization (ODLNMF) and optimized discriminantnon-negative sparse coding (ODNNSC); secondly, to deal with the multimodal distribu-tions of ISAR images, constraints inspired by the clustering based discriminant analysis(CDA) are imposed on LNMF and NNSC to deduce algorithms for ISAR target recog-nition, which are based on subclass discriminant local non-negative matrix factorization(SDLNMF) and subclass discriminant non-negative sparse coding (SDNNSC).SDLNMF and SDNNSC extend the LNMF and NNSC decomposition, respectively, byembedding the subclass discriminant constraints, and reformulate the cost function toachieve discriminant projections. Simulations demonstrate the effectiveness and ro-bustness of the proposed method.In chapter5, algorithms for manifold feature extraction of ISAR images based onorthogonal neighborhood subclass discriminant projections (ONSDP) are presented. Atfirst, ONSDP is proposed to extract low-dimensional feature from high-dimensional andmultimodal ISAR images, which incorporates local topology preserving and subclassdiscriminant dimensionality reduction. Moreover, an orthogonal constraint is introducedto preserve geometric structures of ISAR images; secondly, to explore nonlinear struc-ture of ISAR targets, we add kernel function to ONSDP and propose kernel orthogonalneighborhood subclass discriminant projections (KONSDP), which replaces innerproduct with RBF kernel function and maps implicitly original nonlinear data to a high-er dimensional linearly separable space. Derivation process and verification by simula-tions are given later; thirdly, regarding an ISAR image as the second-order tensor, wepropose algorithm steps as well as derivation process of tensor subclass discriminantanalysis (TSDA) and tensor orthogonal neighborhood subclass discriminant projections(TONSDP), which preserve the structure information between the rows and columns inthe ISAR image and takes advantage of subclass discriminant analysis. Optimization oftensor subspace is finally converted to the problem of generalized eigenvectors Simula-tions verificate the advantage of these methods in target recognition rate.Charpter6summerizes this dissertation and discusses the future work.更多还原