基于物理模型的极化SAR自动目标识别研究

项目名称： 基于物理模型的极化SAR自动目标识别研究

项目编号： No.61201292

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 王英华

作者单位： 西安电子科技大学

项目金额： 23万元

中文摘要： 针对合成孔径雷达（SAR）自动目标识别（ATR）中的两个难点问题，即复杂环境下的目标检测与扩展工作条件下的稳健目标识别，本项目将基于高分辨率全极化SAR（PolSAR）数据，从物理散射机理模型与散射中心模型两种物理模型出发，对以下三方面内容展开研究。首先，基于参数化物理散射机理模型集与稀疏重构思想，研究具有模型自适应能力的极化目标分解方法，以分析目标与杂波的散射机理，为后续的目标检测与识别奠定基础；其次，通过分析散射机理，建立包含多种基本散射类型的复合目标的参数化模型，并结合该模型构造极化检测量，以实现复杂环境下基于物理散射机理的复合目标检测；最后，拟联合图像域与频率域的稀疏性估计属性散射中心模型的物理参数，并提取散射中心极化特征，研究基于属性稳定散射中心的极化稳健识别特征提取，以结合极化信息实现扩展工作条件下的稳健识别。为PolSAR数据在国民经济与国防建设中的应用提供技术支撑。

中文关键词： 极化合成孔径雷达；散射机理；散射中心模型；目标检测；目标识别

英文摘要： Target detection under complex environment and target recognition under extended operating conditions are still two difficult issues in synthetic aperture radar (SAR) automatic target recognition. In order to resolve these two issues, this project will make use of the high-resolution fully polarimetric SAR (PolSAR) images. Starting from two kinds of physical models, i.e., the physical scattering mechanism model and the scattering center model, the project will be performed from the following three aspects. Firstly, based on the parametric scattering mechanism model set and the idea of sparse reconstruction, the polarimetric target decomposition with the capability of model adaptation will be studied. It will be used to analyze the scattering mechanisms of targets and clutters, which provide the basis for the following study on target detection and recognition. Secondly, by analyzing the physical scattering mechanisms, we plan to construct the parametric model for the composite target, which includes several different elementary scattering mechanisms. Then the polarimetric test statistic will be designed by considering the composite target model. In this way, the composite target detection approach based on physical scattering mechanisms will be studied in order to detect composite targets under the complex envir

英文关键词： Polarimetric synthetic aperture radar；scattering mechanism；scattering center model；target detection；target recognition