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

模式识别(心理学) 人工智能 计算机科学 聚类分析 自动目标识别 分类器(UML) 支持向量机 雷达 均值漂移 计算 合成孔径雷达 算法 电信
作者
Pengcheng Guo,Zheng Liu,Jingjing Wang
出处
期刊:Chinese Journal of Systems Engineering and Electronics [Institute of Electrical and Electronics Engineers]
卷期号:31 (6): 1152-1159 被引量:9
标识
DOI:10.23919/jsee.2020.000087
摘要

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.
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