Comparative Study of Planar Coil EMI Sensors for Inversion-Based Detection of Buried Objects

Ambruš, Davorin; Vasić, Darko; Bilas, Vedran

doi:10.1109/jsen.2019.2944752

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…Additionally, highly magnetic soils pose a challenge for accurate MPT measurement due to the electromagnetic behaviour of soils [44]. The issues of lateral and multiple coil spacing has been described in [10], [12] and is the topic of ongoing research in [19]. Once these issues are further understood, laboratory-based instruments for accurately measuring MPTs such as the one presented in this paper, will be vital in aiding the reduction of false alarms in the field.…”

Section: Discussionmentioning

confidence: 97%

“…Recent progress in mathematical theory has demonstrated that the MPT is the coefficient array in the first term in an asymptotic expansion of the perturbed field [6]- [9]. Although the motivation of this paper is toward humanitarian demining, the MPT and Electromagnetic Induction Spectroscopy have also been successfully applied widely in MD, for example to the detection of unexploded ordnance [10]- [12], walk-through metal detectors [13], metal recycling [14], workpiece recognition [15], non-destructive testing [16]- [18] and buried object detection [19]. MPT and broadband inductive sensing have also been previously proposed for landmine detection [20]- [25].…”

mentioning

confidence: 99%

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Measuring the Magnetic Polarizability Tensor Using an Axial Multi-Coil Geometry

et al. 2021

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The Magnetic Polarizability Tensor (MPT) is a representative property of an electrically conducting or magnetic object that includes information about the object's characteristics such as, shape, size and material. The MPT is especially relevant to metal detection (MD) and can be used to improve MD performance by helping to distinguish between objects. This paper describes an instrument intended to measure the MPT of objects such as anti-personnel landmines and metallic clutter, up to 130 mm in diameter. The instrument uses a novel multi-coil geometry to generate a uniform electromagnetic field over the volume containing the test object to accurately determine the MPT. Performance tests of the system shows peak variance in the MPT is approximately 15 mm 3 . Typical experimental repeatability is better than one percent for tests involving copper disks. Additionally, simulated data as well as previously published simulated and experimental data are used as a validation method of the experimental results. Good agreement between these and the measured MPTs of example targets are seen, proving the system's capability of characterizing metallic objects.

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Section: Discussionmentioning

confidence: 97%

mentioning

confidence: 99%

Measuring the Magnetic Polarizability Tensor Using an Axial Multi-Coil Geometry

et al. 2021

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“…A magnetic tensor sensor for gradient-based localization of ferrous objects in a geomagnetic field was demonstrated in [76]. The use of planar coil EMI sensors have successfully detected obscured objects using inversion modeling [77]. The inversion method has the advantage of inherently estimating location and orientation information in addition to classification.…”

Section: Statistical Classification Of Metallic Objects In Seawater U...mentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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“…We also perform the sensitivity analysis on the principal axes polarizability. Formula (9) and (10) are used as the model function of the principal axes polarizability. The process of sensitivity analysis in this paper is shown in Fig.4 and the results of sensitivity analysis are shown in next section.…”

Section: The New Matrix a (I)mentioning

confidence: 99%

A Comparative Study of Inversion Optimization Algorithms for Underground Metal Target Detection

Wan

Wang

et al. 2020

IEEE Access

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Underground metal target detection refers to estimating the properties of underground metal targets based on a set of observed data. Electromagnetic induction (EMI) method and the least-squares inversion provide the data acquisition method and the parameter estimation method for the detection, respectively. As an important part of least-squares inversion, optimization algorithms directly affect the efficiency of the least-squares inversion. To improve the efficiency of underground metal target detection, it is necessary to compare the performance of different optimization algorithms. In this paper, we analyzed the characteristic of the complex EMI forward model using sensitivity analysis and inverse sensitivity analysis at first. Then, the EMI forward model and least squares were used to build the objective function. The estimation error, run time and number of iterations of six numerical optimization algorithms were compared under different signal-to-noise ratios (SNRs) and under different data acquisition spacing. The algorithms include gradient descent, steepest descent, Newton's method, Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, conjugate gradient method and Levenberg-Marquardt (LM) algorithm. A simulation platform was established to generate observed data and compare the optimization algorithms. The results of algorithm comparison showed that the BFGS, conjugate gradient and LM algorithm can efficiently and accurately estimate the properties of underground metal targets and the LM algorithm has the shortest run time and the least number of iterations. Finally, we carefully analyzed the time consumption of each optimization algorithm. The results show that the calculation of the gradient and step length selection greatly affect the efficiency of the optimization algorithms. INDEX TERMS electromagnetic induction (EMI), least-squares inversion, optimization algorithm, sensitivity analysis, underground metal target detection.

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Comparative Study of Planar Coil EMI Sensors for Inversion-Based Detection of Buried Objects

Cited by 17 publications

References 29 publications

Measuring the Magnetic Polarizability Tensor Using an Axial Multi-Coil Geometry

Measuring the Magnetic Polarizability Tensor Using an Axial Multi-Coil Geometry

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

A Comparative Study of Inversion Optimization Algorithms for Underground Metal Target Detection

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