Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
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DOI：
10.1002/nme.6688
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发表时间：
2021-04
影响因子：
2.9
通讯作者：
P. Ledger;B. A. Wilson;A. A. S. Amad-A.;W. Lionheart
中科院分区：
文献类型：
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作者：
P. Ledger;B. A. Wilson;A. A. S. Amad-A.;W. Lionheart
通过改进金属探测，及早发现枪支和刀具等恐怖主义威胁物体，有可能减少袭击次数，改善公共安全和安保。为了实现这一点，有相当大的潜力，使用由金属检测器施加和测量的场来区分不同的形状和不同的金属，因为隐藏在场扰动内的是对象表征信息。磁极化张量（MPT）提供了一种经济的金属物体特性，可以针对不同的威胁和非威胁物体进行计算，并具有既定的理论背景，表明感应电压是隐藏物体MPT系数的函数。在这篇文章中，我们描述了额外的表征信息，在一个频率范围内的感应电压的测量提供相比，在一个单一的频率测量。我们称这种对象的特性的MPT光谱签名。然后，我们提出了一系列的替代旋转不变量的目的，使用MPT光谱签名的隐藏对象进行分类。最后，我们包括计算的MPT光谱特征表征的现实威胁和非威胁对象的例子，可用于训练机器学习算法进行分类。
The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal detector to discriminate between different shapes and different metals since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characterisation of metallic objects that can be computed for different threat and non‐threat objects and has an established theoretical background, which shows that the induced voltage is a function of the hidden object's MPT coefficients. In this article, we describe the additional characterisation information that measurements of the induced voltage over a range of frequencies offer compared with measurements at a single frequency. We call such object characterisations its MPT spectral signature. Then, we present a series of alternative rotational invariants for the purpose of classifying hidden objects using MPT spectral signatures. Finally, we include examples of computed MPT spectral signature characterisations of realistic threat and non‐threat objects that can be used to train machine learning algorithms for classification purposes.