基于H/A/α分解的全极化HRRP目标识别方法

针对现有基于H/A/α分解提取全极化高分辨率距离像（high range resolution profile， HRRP）特征的方法都没有考虑度量尺度对所提取特征性能影响的问题，提取了平均度量尺度下的特征子集，给出联合动态互信息概念用于选择最优平均度量尺度，并剔除特征子集中的冗余特征；在此基础上，结合Bagging和Boosting算法，提出一种宽带全极化雷达目标识别方法；最后在多类飞机目标HRRP样本集上验证了该方法的有效性。

Target recognition of fully polarimetric HRRP based on H/A/α decomposition

It is still an open issue how to deal with the effect of measurement scale on features in the domain of full-polarized high range resolution profile (HRRP) feature extraction based on H/A/α decomposition. To solve it, the feature subsets are extracted under the mean measurement scale. Meanwhile, the joint dynamic mutual information is introduced to select the optimal mean measurement scale and exclude the redundant features in the subsets. Finally, a novel target recognition approach based on full-polarized HRRP with the combination of Bagging and Boosting comes into being. The experiments on multi-class plane targets validate the proposed method’s efficiency.