Improving reliability for classification of metallic objects using a WTMD portal

Makkonen, Jarmo; Marsh, Liam A; Vihonen, Juho; Järvi, Ari; Armitage, David; Visa, Ari; Peyton, A. J.

doi:10.1088/0957-0233/26/10/105103

Cited by 16 publications

(38 citation statements)

References 17 publications

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“…The PODP algorithm has been implemented in the Python interface to the high order finite element solver NGSolve package led by the group of Schöberl 23,24,27 available at https://ngsolve.org. The snapshots are computed by solving (9) and (11) using NGSolve and their H (curl) conforming tetrahedral finite element basis functions of order p on meshes of spacing h. 26 Following the solution of (16), and the application of (13), the coefficients of  [ B, ] 3 follow by simple postprocessing of (5). If desired, PODP output certificates can also be efficiently computed using the approach described in Section 4.3.…”

Section: Numerical Examples Of Podpmentioning

confidence: 99%

“…The background field is generated by a set of transmit coils and the perturbed magnetic field is measured as a voltage in a set of receive coils. Systems of transmit and receive coils have been designed to carefully measure the spectral signature of the MPT in the laboratory 6,7 as well as for specific applications including walk through metal detectors at transport hubs, 8,9 the identification of antipersonnel landmines, 10 in-line scanning, 11,12 and the recycling of valuable metals. 13 Nevertheless, these systems will lead to noise and errors in the MPT coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…14 There will also be other errors and noise associated with capacitive coupling with low-conducting objects or soil in the background as well as general noise (e.g., from amplifiers, parasitic voltages in the receive coils and from filtering). 9 The amount of error and noise will vary from application to application and from system to system. For MPT spectral signatures obtained in laboratory the current accuracy is about 1% (e.g., Reference 7) while for walk through metal detectors it is about 5% (e.g., References 8,9), but the accuracy of these systems is improving all the time.…”

Section: Introductionmentioning

confidence: 99%

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Efficient computation of the magnetic polarizabiltiy tensor spectral signature using proper orthogonal decomposition

Wilson

Ledger

2021

Numerical Meth Engineering

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The identification of hidden conducting permeable objects from measurements of the perturbed magnetic field taken over a range of low frequencies is important in metal detection. Applications include identifying threat items in security screening at transport hubs, location of unexploded ordnance, and antipersonnel landmines in areas of former conflict, searching for items of archeological significance and recycling of valuable metals. The solution of the inverse problem, or more generally locating and classifying objects, has attracted considerable attention recently using polarizability tensors. The magnetic polarizability tensor (MPT) provides a characterization of a conducting permeable object using a small number of coefficients, has an explicit formula for the calculation of their coefficients, and a well understood frequency behavior, which we call its spectral signature. However, to compute such signatures, and build a library of them for object classification, requires the repeated solution of a transmission problem, which is typically accomplished approximately using a finite element discretization. To reduce the computational cost, we propose an efficient reduced order model (ROM) that further reduces the problem using a proper orthogonal decomposition for the rapid computation of MPT spectral signatures. Our ROM benefits from a posteriori error estimates of the accuracy of the predicted MPT coefficients with respect to those obtained with finite element solutions. These estimates can be computed cheaply during the online stage of the ROM allowing the ROM prediction to be certified. To further increase the efficiency of the computation of the MPT spectral signature, we provide scaling results, which enable an immediate calculation of the signature under changes in the object size or conductivity. We illustrate our approach by application to a range of homogenous and inhomogeneous conducting permeable objects.

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Section: Numerical Examples Of Podpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient computation of the magnetic polarizabiltiy tensor spectral signature using proper orthogonal decomposition

Wilson

Ledger

2021

Numerical Meth Engineering

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“…Electrical engineers have applied MPTs to a range of practical metal detection applications, including walk through metal detectors, in line scanners, and demining, eg, previous studies, see also our article for a recent review but without knowledge of the explicit formula described above. Engineers have made a prediction of the form of the response for multiple objects, eg, existing study but without an explicit criteria on the size or the distance between the objects in order for the approximation to hold.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of multiple conducting permeable objects in metal detection by polarizability tensors

Ledger

Lionheart

Amad

2018

Math Methods in App Sciences

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Realistic applications in metal detection involve multiple inhomogeneousconducting 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.

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“…If we know the tensor of the object beforehand, we can use it to distinguish different metallic objects from one another purely from their magnetic response. This approach has significant application in areas such as walk-through metal detectors [3], [4] and in landmine detection and clearance [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the thin-skin approximation boundary element method for electromagnetic induction scattering problems

O’Toole

Davidson

Marsh

et al. 2016

2016 IEEE Sensors Applications Symposium (SAS)

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Abstract-A conductive object subject to an applied varying magnetic field will emit a secondary magnetic field or scattered field. The scattered field is dependent on the geometry of the object and its material properties (conductivity, permeability, etc). If we can calculate the scattered field for a given geometry (the scattering problem), we can infer the material properties from the detected electromagnetic response. Our motivation is the production of induction based classifiers for object and material classification. Applications include sorting of high value scrap metal and identifying UXO from clutter in landmine clearance. To this end, we require methods of solving the scattering problem quickly and accurately.In this paper, we evaluate the thin-skin approximation boundary element method. The method offers a particularly compact formulation of the scattering problem which is quick to solve. We compare this method to the more established finite element method. We find that larger objects at higher frequencies and conductivities appear to give good agreement between the two methods. However, the agreement breaks down for smaller objects even when the frequency or conductivity is relatively high for typical induction based sensing. This is especially true when the object has a complex geometry. This imposes limitations on the practical usefulness of this approach.

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Improving reliability for classification of metallic objects using a WTMD portal

Cited by 16 publications

References 17 publications

Efficient computation of the magnetic polarizabiltiy tensor spectral signature using proper orthogonal decomposition

Efficient computation of the magnetic polarizabiltiy tensor spectral signature using proper orthogonal decomposition

Characterisation of multiple conducting permeable objects in metal detection by polarizability tensors

Evaluation of the thin-skin approximation boundary element method for electromagnetic induction scattering problems

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