Simple phenomenological models for wideband frequency-domain electromagnetic induction

Miller, James D.; Bell, Thomas H.; Soukup, J.; Keiswetter, Dean

doi:10.1109/36.927452

Cited by 73 publications

(41 citation statements)

References 8 publications

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“…The peak quadrature response occurs at ω = 2πf = κ 2 . Equation (20) has been found to provide a reasonably good representation of the response near the peak of the quadrature spectrum for a variety of compact steel objects [18]. This is illustrated in Figure 12, which is a composite of 58 measured quadrature spectra of compact steel targets, including a variety of inert ordnance items, plates, cylinders, I-beam pieces, etc.…”

Section: Parametric Formsmentioning

confidence: 99%

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Phenomenology and Signal Processing for UXO/Clutter Discrimination

Bell¹

2009

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Section: Parametric Formsmentioning

confidence: 99%

“…EMI response curves for compact metal objects such as UXO and much of the clutter do not typically have much structure, and can be represented using rather simple empirical expressions [13,18]. Here, we explore an alternative parametric representation for the decay curves that is motivated by the basic physics of the EMI response.…”

Section: Parametric Formsmentioning

confidence: 99%

Phenomenology and Signal Processing for UXO/Clutter Discrimination

Bell¹

2009

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“…In the inverse problem, several studies have been undertaken in the frequency domain [7,8] as well as in the time domain [9,10]. However, the common point of these researches is the characterization of the buried objects to minimize false alarms due to the presence of nonexplosive metallic objects.…”

Section: Introductionmentioning

confidence: 99%

An Emi Inversing Problem for Landmine Characterization Based on Improved Particle Swarm Optimization and Finite Element Analysis

Matriche¹,

Féliachi²,

Zaoui³

et al. 2013

PIER B

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Abstract-This paper discusses the characterization of landmine by using the electromagnetic induction technique (EMI). The proposed approach is based on the identification of the physical and geometrical properties of a landmine, from the sensor response. But in such an identification, the inverse problem is unavoidable. At first, we begin by simulating the landmine signature by solving a direct problem using the finite element method which constitutes the direct model. After that, we determine the landmine characteristics by using an inverse model based on a cost function optimization. This model is based on an iterative process which coupling finite element analysis and Particles Swarm Optimization (PSO). In this step, we apply two PSO techniques: the Standard PSO (SPSO) and the Improved PSO (IPSO), and discuss the problem of local minima of the cost function. The proposed iterative model is applied to determine the conductivity, geometry, and depth of metallic landmine from its signature measured by EMI. The numerical solution gives good results for the identification of landmine.

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“…After normalization, the response models proposed by Miller et al [91] are used to fit the curve. The 3-parameter model is given by…”

Section: Landmine Detection Using Wemimentioning

confidence: 99%

“…In addition to the 4 features provided by the model, 3 spread features [91] Existing local classifier fusion methods treat the partitioning of the feature space and the selection/fusion of local expert classifiers as two independent processes that are performed sequentially (refer to section 2.7). However, these two tasks are not independent, and their optimization should be combined.…”

Section: Landmine Detection Using Wemimentioning

confidence: 99%

A generic framework for context-dependent fusion with application to landmine detection.

Abdallah¹

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Whoever slays a soul, ... , it is as though he slew all men; and whoever keeps it alive, it is as though he kept alive all men Quran 5:32As afootballer I can't imagine life without the use of one of my legs ... Sadly this is exactly what happens to thousands of children every yearwhen they accidentally step on a landmine. Ryan Giggs iii ACKNOWLEDGEMENTSI would like to express my deepest gratitude to my advisor, Dr. Hichem Frigui, for giving me the opportunity to be a member in his research group and for his support over the course of this work. He provided a very rich working environment with many opportunities to develop new ideas, work in promising applications and get experience in diverse areas. I am indebted to his support and help. For complex detection and classification problems, involving data with large intraclass variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier.Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers' worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets.The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second ...

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Simple phenomenological models for wideband frequency-domain electromagnetic induction

Cited by 73 publications

References 8 publications

Phenomenology and Signal Processing for UXO/Clutter Discrimination

Phenomenology and Signal Processing for UXO/Clutter Discrimination

An Emi Inversing Problem for Landmine Characterization Based on Improved Particle Swarm Optimization and Finite Element Analysis

A generic framework for context-dependent fusion with application to landmine detection.

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