Radar Target Recognition Based on Polarization Invariant

Rui Zhang, Linxi Zhang, Yufei Wang, Yu-fen Xie
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引用次数: 3

Abstract

With the development of radar full polarization measurement technology, target recognition using polarization information has become a research hotspot. Polarization invariants can be used to characterize a target, which can directly indicate the physical property of targets. Previous target recognition research has focused on missiles or aircrafts, but ground vehicles are also a very important category in military targets. In this paper, two tank models were simulated by using FEKO software. The polarization invariants obtained from simulation data are used as an recognition data set. Comparing the results from three different types of recognition algorithms, the average recognition accuracy based on BP neural network is higher than KNN and SVM methods.
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基于偏振不变性的雷达目标识别
随着雷达全偏振测量技术的发展,利用偏振信息进行目标识别已成为研究热点。偏振不变量可以用来表征目标,它可以直接反映目标的物理性质。以往的目标识别研究主要集中在导弹或飞机上,但地面车辆也是军事目标中非常重要的一类。本文利用FEKO软件对两种坦克模型进行了仿真。利用仿真数据得到的极化不变量作为识别数据集。对比三种不同识别算法的结果,基于BP神经网络的平均识别准确率高于KNN和SVM方法。
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