Identification of Metallic Objects using Spectral MPT Signatures: Object Characterisation and Invariants

TL;DR

This paper explores the use of magnetic polarizability tensor (MPT) spectral signatures for metallic object identification, enhancing threat detection by characterizing objects through frequency-dependent measurements and invariants for improved classification.

Contribution

It introduces the concept of MPT spectral signatures and develops rotational invariants for classifying metallic objects, advancing metal detection techniques.

Findings

MPT spectral signatures provide richer object characterization than single-frequency measurements.

Rotational invariants enable effective classification of threat and non-threat objects.

Examples demonstrate the potential for machine learning-based object identification using MPT data.

Abstract

The early detection of terrorist threats, 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 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.