自适应核协同表示在SAR目标识别中的应用

Application of Kernel Function Transformation Collaborative Representation in SAR Target Recognition

摘要:

针对合成孔径雷达(Synthetic Aperture Radar,SAR)目标识别中方位角差距大的训练样本之间存在干扰的问题,对传统协同表示字典进行了改进,得到更适应于当前测试样本且能够降低弱相关原子对系统影响的自适应字典,基于此提出了一种自适应原子选择的核函数变换协同表示算法,并在美国DARPA中的运动和静止目标获取与识别计划公开发布的SAR图像数据库上进行了验证实验。实验结果表明,相较于基于全部训练样本字典的核协同表示模型,基于自适应原子选择的核协同表示方法降低了干扰原子的不良影响,提高了对SAR目标识别的可靠性和鲁棒性。

Abstract:

Aiming at the disturbance of training samples with large aspect gap in SAR target recognition, a kernel function transformation collaborative algorithm based on adaptive atom selection is proposed. This method improves the representation dictionary in the traditional collaborative representation, and gets the adaptive dictionary which is more adaptable to the current test samples and can reduce the influence of the unrelated atoms to the system. The experiments of SAR target recognition based on MSTAR datasets were carried out. The experimental results show that the kernel collaborative representation based on adaptive atom selection is more effective than the kernel collaborative representation model based on all training sample dictionary, which reduces the harmful effect of the interference atoms and further improves the reliability and robustness of the system.