Characterisation of Multiple Conducting Permeable Objects in Metal Detection by Polarizability Tensors

TL;DR

This paper extends the characterization of multiple conducting permeable objects in metal detection using polarizability tensors, providing asymptotic formulas, computational methods, and algorithms for object localization and identification.

Contribution

It introduces a new asymptotic expansion for multiple objects, including inhomogeneous and closely spaced cases, and develops algorithms for object localization and identification.

Findings

Asymptotic expansion accurately models magnetic field perturbations for multiple objects.

Polarizability tensors encode shape and material properties independently of position.

Numerical examples confirm the effectiveness of the asymptotic formulas and algorithms.

Abstract

Realistic applications in metal detection involve multiple inhomogeneous conducting permeable objects and the aim of this paper is to characterise such objects by polarizability tensors. We show that, for the eddy current model, the leading order terms for the perturbation in the magnetic field, due to the presence of $N$ small conducting permeable homogeneous inclusions, comprises of a sum of $N$ terms with each containing a complex symmetric rank 2 polarizability tensor. Each tensor contains information about the shape and material properties of one of the objects and is independent of its position. The asymptotic expansion we obtain extends a previously known result for a single isolated object and applies in situations where the object sizes are small and the objects are sufficiently well separated. We also obtain a second expansion that describes the perturbed magnetic field for inhomogeneous and closely spaced objects, which again characterises the objects by a complex symmetric rank 2 tensor. The tensor's coefficients can be computed by solving a vector valued transmission problem and we include numerical examples to illustrate the agreement between the asymptotic formula describing the perturbed fields and the numerical prediction. We also include algorithms for the localisation and identification of multiple inhomogeneous objects.