摘要
针对合成孔径雷达(synthetic aperture radar,SAR)目标识别问题,提出一种基于贝叶斯压缩感知(Bayesian compressive sensing,BCS)的图像域SAR目标识别方法。该方法首先对SAR图像进行分割预处理,得到目标区图像数据;然后基于BCS模型,根据训练样本构造传感矩阵;求解测试样本相应的稀疏系数矢量,根据稀疏系数矢量中对应训练样本类别元素的L2范数判定目标类型。采用美国运动和静止目标获取与识别(movingand stationary target acquisition and recognition,MSTAR)计划公开发布的SAR目标数据库进行实验,结果表明该方法具有良好的识别效果。
A new approach is developed for synthetic aperture radar （SAR） automatic target recognition based on Bayesian compressive sensing （BCS）. Firstly SAR images are segmented into image data of target zones by constant false alarm rate. Then based on the BCS model, the sensing matrix is constructed by all train- ing sets. The sparse coefficient vectors corresponding to the test samples are solved. Recognition is performed according to the L2 norm corresponding to each of training types of samples in the sensing matrix. Experimental results with the moving and stationary target acquisition and recognition public dataset show that the proposed approach has good recognition effects.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2013年第1期40-44,共5页
Systems Engineering and Electronics
基金
中央高校基本科研业务费(CDJRC11160003)资助课题
关键词
合成孔径雷达
自动目标识别
压缩感知
稀疏
synthetic aperture radar （SAR）
automatic target recognition （ATR）
compressive sensing （CS）
sparse
