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When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range pro-files (HRRPs) of group targets in a beam to overlap,which re-duces the target recognition performance of the radar.In this pa-per,we propose a group target recognition method based on a weighted mean shift (weighted-MS) clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine (SVM) classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to ex-tract the HRRP of each subtarget.Then,the features of the sub-target HRRP are extracted and used as input in the SVM classifi-er to be recognized.Compared to the traditional group target re-cognition method,the proposed method has the advantages of requiring only a small amount of computation,setting paramet-ers automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition per-formance and is more robust against noise than other recogni-tion methods.