基于Bagging-SVM动态集成的多极化HRRP识别

针对多极化高分辨率一维距离像（high range resolution profile, HRRP）在目标识别过程中存在计算量和数据量大、识别算法复杂的问题，提出一种基于Bagging-SVM动态集成的目标识别方法。该方法首先提取多极化HRRP平移不变特征向量，然后运用Bagging方法结合基于动态互信息的特征选择方法生成基分类器，最后引入基分类器差异度进行选择性集成。实验验证该方法在缩减数据规模和计算量的同时，能有效利用多极化特征信息，得到较高的分类正确率，并且松弛了HRRP目标的姿态敏感性。

HRRP recognition for polarization radar based on Bagging-SVM dynamic ensemble

As for the problems of calculation,data and recognition algorithm complexity of multi-polarized HRRP in target recognition field,a recognition approach based on Bagging-SVM dynamic ensemble is proposed.Firstly,multi-polarized HRRP’s shift invariant feature vectors are extracted.Then Bagging and feature selection methods based on dynamic mutual information are used to train the base classifier.Finally,base classifier diversity is introduced to carry out selective ensemble.The experiments validates that the method performs well in reducing the data dimensions,calculation and has a better recognition correct rate,at the same time,it lets down the HRRP’s aspect sensitivity.