  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.
