Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition, it is quite possible for the high-resolution range profiles (HRRPs) of group targets in a beam to overlap, which reduces the target recognition performance of the radar. In this paper, 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 extract the HRRP of each subtarget. Then, the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized. Compared to the traditional group target recognition method, the proposed method has the advantages of requiring only a small amount of computation, setting parameters 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 performance and is more robust against noise than other recognition methods.