Abstract: In order to recognize SAR target accurately, an identification method based on sparse representation is proposed. The training samples after dimensionality reduction using principal component analysis are used to build a sparse linear model. The sparse coefficient solution [x] of the test sample is solved by [?1]-minimization. The identification task is solved by utilizing the sparse distribution of the sparse coefficient. Experimental results with MSTAR dataset verify that the identification method based on sparse representation in a certain characteristic dimension can obtain good recognition performance, and the recognition rate can reach more than 98% without knowing the target azimuth.

Key words: Synthetic Aperture Radar（SAR）, target recognition, sparse representation, [?1]-minimization

摘要： 为了准确地进行SAR图像目标识别，提出一种基于稀疏表示的SAR目标识别方法，在用主成分分析（PCA）进行降维的前提下，利用降维后的训练样本构建稀疏线性模型，通过[?1]范数最优化求解测试样本的稀疏系数解[x]，利用系数的稀疏性分布进行目标的分类识别。基于MSTAR数据进行了仿真验证，实验证明，基于稀疏表示的SAR目标识别方法在一定的特征维数下能够获得很好的识别性能，在目标方位角未知的情况下识别率仍可达到98%以上。

关键词: 合成孔径雷达（SAR）, 目标识别, 稀疏表示, [?1]范数最优化