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.

中文翻译：

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基于HRRP和加权均值漂移聚类的雷达群目标识别

将测距高分辨率雷达应用于目标识别时，波束中的群目标的高分辨率测距曲线（HRRP）很有可能会重叠，从而降低了雷达的目标识别性能。在本文中，我们提出了一种基于加权均值漂移（加权MS）聚类方法的群体目标识别方法。在训练阶段，基于模板数据库提取子目标特征，通过仿真或数据获取建立子模板特征，并将特征馈入支持向量机（SVM）分类器以获得分类器参数。在测试阶段，利用加权MS算法提取每个子目标的HRRP。然后，提取子目标HRRP的特征，并将其用作SVM分类器中的输入以进行识别。与传统的群体目标识别方法相比，该方法具有计算量少，自动设置参数，不需要目标运动的优点。基于实测数据的实验结果表明，与其他识别方法相比，本文提出的方法具有更好的识别性能和更强的抗噪能力。