为利用合成孔径雷达(synthetic aperture radar，SAR)目标不同特征数据间的相关性与互补性，提出一种基 于多特征的Tikhonov 正则化核函数协同表示(multi-feature kernel collaborative representation- based classification with tikhonov regularization，MFKCRT)算法。采用美国运动和静止目标获取与识别(moving and stationary target acquisition and recognition，MSTAR)计划公开发布的SAR 图像数据库进行实验，实现核函数变换空间上的多特征融合协同表示 识别。实验结果表明：该算法相较于基本的协同表示，具有更优的可靠性与鲁棒性。
In order to exploit the correlation and complementarity between different feature data of synthetic aperture radar (SAR) target, a Tikhonov regularization kernel function collaborative representation algorithm based on multiple features is proposed. The SAR image database released by the moving and stationary target acquisition and recognition (MSTAR) program of the United States is used in the experiment. The multi-feature fusion collaborative representation and recognition on the kernel function transformation space is realized. Experimental results show that the proposed algorithm is more reliable and robust than the basic collaborative representation.