Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features
Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features
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DOI：
10.1109/lgrs.2015.2439393
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发表时间：
2015-09-01
影响因子：
4.8
通讯作者：
Griffiths, Hugh
中科院分区：
文献类型：
--
作者：
Fioranelli, Francesco;Ritchie, Matthew;Griffiths, Hugh
在这封信中，我们提出了使用实验人类微多普勒签名数据收集的多基地雷达系统区分非武装和潜在的武装人员走沿着不同的轨迹。讨论了从微多普勒特征的频谱图中提取合适特征的不同方法，特别是经验特征，例如多普勒带宽、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准确性（取决于人的行走轨迹在90%至97%之间）。本文还讨论了不同方位角对分类性能的影响以及多基地信息结合的好处。
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.