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基于稀疏表示的SAR图像目标识别方法
引用本文:刘 振,姜 晖,王粒宾.基于稀疏表示的SAR图像目标识别方法[J].计算机工程与应用,2014(10):212-215,232.
作者姓名:刘 振  姜 晖  王粒宾
作者单位:电子工程学院信息工程系,合肥230037
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 ξ1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  ξ1范数最优化

SAR ATR method based on sparse representation
LIU Zhen,JIANG Hui,WANG Libin.SAR ATR method based on sparse representation[J].Computer Engineering and Applications,2014(10):212-215,232.
Authors:LIU Zhen  JIANG Hui  WANG Libin
Affiliation:( Department of Information Engineering, Electronic Engineering Institute of PLA, Hefei 230037, China)
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.
Keywords:target recognition  sparse representation  ?1-minimization
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