Journal article 493 views 135 downloads
Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
,
Ben Wilson,
Alan Amad ,
William R. B. Lionheart
International Journal for Numerical Methods in Engineering, Volume: 122, Issue: 15, Pages: 3941 - 3984
Swansea University Authors:
Ben Wilson, Alan Amad
-
PDF | Version of Record
© 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License
Download (6.28MB)
DOI (Published version): 10.1002/nme.6688
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 fields applied and measured by a metal det...
| Published in: | International Journal for Numerical Methods in Engineering |
|---|---|
| ISSN: | 0029-5981 1097-0207 |
| Published: |
Wiley
2021
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa60833 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 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. |
|---|---|
| Keywords: |
finite element method; machine learning; magnetic polarizability tensor; metal detection; object classification; reduced order model; spectral; validation |
| College: |
Faculty of Science and Engineering |
| Funders: |
Engineering and Physical Sciences Research Council. Grant Numbers: EP/R002134/2, EP/R002177/1;
Royal Society. Grant Number: Royal Society Wolfson Research Merit Award |
| Issue: |
15 |
| Start Page: |
3941 |
| End Page: |
3984 |