【作者】 韩萍；

【导师】 王兆华； 吴仁彪；

【作者基本信息】 天津大学 ， 信号与信息处理， 2004， 博士

【摘要】 基于合成孔径雷达（Synthetic Aperture Radar,简称SAR）的自动目标识别（Automatic Target Recognition,简称ATR）技术在战场感知方面非常重要，已成为国内外研究的热门课题。近些年来，雷达目标识别在特征提取、目标模式分类、目标识别算法的实现等技术领域取得了不同程度的进步，已成功应用于星载和机载合成孔径雷达地面侦察、精确制导等领域。本文综述了自动目标识别技术的发展现状，深入研究了基于SAR的自动目标识别及其相关技术，在此基础上，提出了一些新方法，具体如下：1．提出了一种新的SAR图像配准新方法。这是一种基于非线性最小二乘的图像配准方法，除了有效地实现目标图像的位置对准、幅度差异补偿外，还能补偿图像间的平均偏差。该方法具有速度快、匹配精度高的特点。2．提出了一种基于最佳线性变换的SAR目标识别方法。它是将样本的幅频特性进行最佳线性变换并与支持矢量机(Support Vector Machine,简称SVM)相结合，完成目标识别。该方法保证了目标的平移不变性，有效地提高了识别率、训练速度和识别速度，降低了对目标方位估计精度的要求。3．将几种非线性特征提取技术首次应用于SAR自动目标识别中。分别提出了基于KPCA（Kernel Principle Component Analysis,简称KPCA）和KFD(Kernel Fisher Discriminant,简称KFD)的SAR目标特征提取与识别方法，这两种方法都是在非线性空间内提取样本特征并与SVM相结合完成目标识别。它们具有识别率高，速度快，推广性好，对目标方位变化不敏感等特点。4．提出了两种SAR目标与阴影图像的分割方法。一种是基于Weibull分布的SAR目标与阴影图像的分割方法。另一种是改进的Markov模型SAR目标与阴影图像的分割方法。前一种方法简单易行，与一般的双参数恒虚警方法相比，具有更好的分割质量。后一种方法在保证分割质量的同时有效地提高了分割速度。更多还原

【Abstract】 SAR (Synthetic Aperture Radar) ATR (Automatic Target Recognition) is crucial to the success of battlefield awareness and has become a very hot research topic. In recent years, radar target recognition has made steady progress in many fields, including feature extraction, target classification and recognition. Some ATR systems have been built and have been successfully used in the areas like ground detecting and precision guidance in spaceborne/airborne SAR.This thesis first reviews the ATR fundamentals and the state-of-the-art development of SAR ATR techniques. Main contributions include:First, a novel image alignment approach is proposed, which is an efficient matching method based on non-linear least square (NLS) fitting. The alignment, amplitude as well as average bias compensation can be done simultaneously in the frequency domain. The new preprocessing method exhibits better performance in matching accuracy and matching speed. Secondly, an optimal linear transform based SAR ATR approach is proposed, which takes optimal linear transform over the magnitude frequency response of target samples and uses the SVM (Support Vector Machine) as the classifier. This method exhibits shift-invariance property and can effectively improve Pcc (Probability of Correct Classification) and training as well as testing speed. Moreover, it can lower down the requirement of bearing estimation accuracy of targets. Thirdly, kernel feature extraction is first applied to SAR ATR. The target feature is extracted by using KPCA (Kernel Principal Component Analysis) or KFD (Kernel Fisher Discriminant) and then classified by SVM. Experimental results demonstrate that the proposed methods can achieve higher Pcc, faster training and testing speed, and better generalization ability. In addition, they are not sensitive to the uncertainty of the target aspect.Finally, two approaches are proposed for SAR target and shadow segmentation. One is a simple segmented method based on Weibull distribution, which shows better performance than Gaussian distribution based methods. The other is an improved method based on MRF (Markov Random Field), which can improve the processing speed while maintaining image segmentation quality.更多还原