Identification of Metallic Objects using Spectral MPT Signatures: Object Characterisation and Invariants
This research aims to improve metal detection for public safety and security by enabling better discrimination between different shapes and metals, which is an incremental improvement for security applications.
This paper explores using Magnetic Polarizability Tensor (MPT) spectral signatures, derived from induced voltage measurements across a range of frequencies, to characterize metallic objects. It proposes new rotational invariants for classifying hidden objects and provides computed MPT spectral signatures for threat and non-threat objects.
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.