A Discrimination Algorithm for UXO Using Time Domain Electromagnetics

Pasion, Leonard R.; Oldenburg, Douglas W.

doi:10.4133/jeeg6.2.91

Cited by 146 publications

(92 citation statements)

References 7 publications

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“…is calculated by using the decaying two-dipole approximation outline in Pasion and Oldenburg [2001a]. The response of a compact buried target can be approximated by a 13 element model vector:…”

Section: Generation Of Synthetic Tem Datamentioning

confidence: 99%

Evaluating the Effects of Magnetic Susceptibility in UXO Discrimination Problems

Pasion¹,

Billings²,

Oldenburg³

2002

Symposium on the Application of Geophysics to Engineering and Environmental Problems 2002

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Past unexploded ordnance (UXO) clearance projects have demonstrated that the ability to detect UXO can be seriously hindered by the presence of magnetic rocks and soils. In particular, magnetic material affects the decay curve characteristics in electromagnetic surveys and this can adversely affect inversion calculations that try to estimate parameters of the UXO or answer the question of whether the item is UXO or scrap. Our research is in two parts. Firstly, we carry out numerical 1D forward modelling to evaluate the distortions of signals due to laterally uniform magnetic material. In the second part of the talk we examine how the distortions in the signal affect our inversion algorithms used for UXO discrimination.

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Section: Generation Of Synthetic Tem Datamentioning

confidence: 99%

Evaluating the Effects of Magnetic Susceptibility in UXO Discrimination Problems

Pasion¹,

Billings²,

Oldenburg³

2002

Symposium on the Application of Geophysics to Engineering and Environmental Problems 2002

Self Cite

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“…In the technical methodology, EMI technique is becoming a technology with great potential [4][5][6]. EMI devices are used either in time domain or frequency one.…”

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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“…A broad array of modeling approaches is available that meets these criteria, from exact numerical models based on finite element and finite difference approaches to models that approximate the buried object as a magnetic dipole. We have chosen to implement the latter "dipole model," which is a decision motivated by the practical need for rapid data processing and by the ubiquity of its use in the UXO detection and classification literature [16]- [22]. We note that more accurate models of the physics of UXO-like targets are available, and in some sense, the dipole model could be considered the first-order application of the techniques developed in this paper, which are extensible to any parameterized model representing UXO.…”

Section: Introductionmentioning

confidence: 99%

Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models

Aliamiri¹,

Stalnaker

Miller³

2007

IEEE Trans. Geosci. Remote Sensing

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Abstract-We used kernel density estimation (KDE) methods to build a priori probability density functions (pdfs) for the vector of features that are used to classify unexploded ordnance items given electromagnetic-induction sensor data. This a priori information is then used to develop a new suite of estimation and classification algorithms. As opposed to the commonly used maximumlikelihood parameter estimation methods, here we employ a maximum a posteriori (MAP) estimation algorithm that makes use of KDE-generated pdfs. Similarly, we use KDE priors to develop a suite of classification schemes operating in both "feature" space as well as "signal/data" space. In terms of feature-based methods, we construct a support vector machine classifier and its extension to support M -ary classification. The KDE pdfs are also used to synthesize a MAP feature-based classifier. To address the numerical challenges associated with the optimal data-space Bayesian classifier, we have used several approximation techniques, including Laplacian approximation and generalized likelihood ratio tests employing the priors. Using both simulations and real field data, we observe a significant improvement in classification performance due to the use of the KDE-based prior models.

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A Discrimination Algorithm for UXO Using Time Domain Electromagnetics

Cited by 146 publications

References 7 publications

Evaluating the Effects of Magnetic Susceptibility in UXO Discrimination Problems

Evaluating the Effects of Magnetic Susceptibility in UXO Discrimination Problems

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

Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models

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