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.

中文翻译：

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使用光谱磁极化张量特征识别金属物体：物体特征和不变量

通过改进金属探测及早发现枪支和刀具等恐怖威胁物体，有可能减少袭击次数并改善公共安全和安保。为了实现这一点，使用金属探测器施加和测量的场来区分不同形状和不同金属具有相当大的潜力，因为隐藏在场扰动中的是物体特征信息。磁极化张量 (MPT) 提供了金属物体的经济表征，可以针对不同的威胁和非威胁物体进行计算，并且具有既定的理论背景，这表明感应电压是隐藏物体的 MPT 系数的函数。在本文中，我们描述了与在单个频率下的测量相比，在一系列频率上测量感应电压所提供的附加特征信息。我们称这种物体特征为 MPT 光谱特征。然后，我们提出了一系列替代旋转不变量，目的是使用 MPT 谱签名对隐藏对象进行分类。最后，我们包括了真实威胁和非威胁对象的计算 MPT 频谱特征特征的示例，这些示例可用于训练机器学习算法以进行分类。我们提出了一系列替代旋转不变量，目的是使用 MPT 谱签名对隐藏对象进行分类。最后，我们包括了真实威胁和非威胁对象的计算 MPT 频谱特征特征的示例，这些示例可用于训练机器学习算法以进行分类。我们提出了一系列替代旋转不变量，目的是使用 MPT 谱签名对隐藏对象进行分类。最后，我们包括了真实威胁和非威胁对象的计算 MPT 频谱特征特征的示例，这些示例可用于训练机器学习算法以进行分类。