Ledger, P ORCID: https://orcid.org/0000-0002-2587-7023, Wilson, BA, Amas, AAS and Lionheart, WRB (2021) Identification of metallic objects using spectral MPT signatures: object characterisation and invariants. International Journal of Numerical Methods in Engineering.

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Abstract

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 felds 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 coeffcients. In this paper, we describe the additional characterisation information that measurements of the induced voltage over a range of frequencies offer compared to 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.

Item Type: Article
Additional Information: This is the author accepted manuscript (AAM). The final published version of record will be available via Wiley at https://onlinelibrary.wiley.com/journal/10970207 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: Finite element method; Magnetic polarizability tensor; Machine learning; Metal detection; Object classification; Reduced order model; Spectral; Validation.
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 06 Apr 2021 09:36
Last Modified: 04 Jun 2021 09:48
URI: https://eprints.keele.ac.uk/id/eprint/9322

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