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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features
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Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features

机译：用于微多普勒和奇异值分解特征的NetRAD多基地雷达对无人武装/武装人员的分类

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摘要

In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.
机译：在这封信中，我们介绍了使用由多基地雷达系统收集的实验性人类微多普勒签名数据来区分沿着不同轨迹行走的无武装人员和潜在武装人员。讨论了从微多普勒签名的频谱图中提取合适特征的不同方法，特别是经验特征（如多普勒带宽，周期性等）以及从奇异值分解（SVD）向量中提取的特征。与使用四个经验特征相比，使用单个基于SVD的特征可以实现武装人员与非武装人员的高分类精度（取决于人员的行走轨迹，介于90％和97％之间）。在这封信中还评估了不同纵横比对分类性能的影响以及组合多静态信息的好处。

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