基于SVM的小样本不均衡HRRP舰船目标分类方法OA北大核心CSTPCD

In view of the small-sample imbalance in the classification of measured HRRP(high resolution range profile)military ships and civilian ships,a feature extraction method combining Relief algorithm and PCA(principal component analysis)algorithm is proposed,and the oversampling algorithm and error iterative weighting method are introduced to improve the SVM classifier.In this classification method,the original high-dimensional HRRP image is subjected to preprocessing and feature subspace weight,which enhances the separability of the main features,and the classification effect of the improved SVM classifier is improved significantly after iterative weighting.As a comparison,the classification effect of the adaptive enhanced SVM classifier is analyzed on the same measured HRRP ship target dataset.Experimental results show that the iteratively weighted Smote-SVM classification method with improved kernel space has better recognition effect and adaptability to the attitude sensitivity of HRRP.