HRRP Classification by Using Improved SVM Decision Tree

Abstract

Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance of the SVM decision tree was analyzed. A separability measure which based on the distribution of the training samples was defined, the defined separability measure was applied into the formation of the decision tree, and an improved algorithm for SVM decision tree was proposed. The scheme of using the improved algorithm for SVM decision tree to classify HRRP was given. Experiments using the range profile datasets prove the effectiveness of our scheme.