Scattering of Object Buried below Random Rough Surface—A Monte Carlo Pseudospectral Time-Domain Approach

Liu, Wei; Dai, Yueyang; Yang, Haiyan; Xu, Xiao-Bang

doi:10.1080/02726343.2012.701515

Cited by 3 publications

(18 citation statements)

References 32 publications

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“…Different from the previously published MPSTD analysis, which mainly analyzed the scattering of buried objects under flat or undulated surfaces, this work and the authors' previous one [32] involve a random rough surface. As the first trial to introduce a random rough surface into the Monte-Carlo MPSTD algorithm, the random rough surface is generated in x direction with Gaussian spectrum profile [36,37],…”

Section: 12-1 Generation Of Random Rough Surface Profile and Its Mmentioning

confidence: 85%

“…A sample random rough surface generated is illustrated in Figure 1. As pointed out in [32], the profile y = f (x m ) is generated as a function of evenly distributed x m . But in a MPSTD subdomain that is partially bounded by the rough surface, the profile y mapped is a function of x mapped , which are related to the CGL points in the (ξ, η, ζ) coordinates by the coordinate transformation.…”

Section: 12-1 Generation Of Random Rough Surface Profile and Its Mmentioning

confidence: 99%

“…Since x m are uniformly distributed but x mapped are not, they are normally different; hence the two profiles of y = f (x m ) and y mapped in general do not coincide. To match these two profiles, an interpolation technique is used as described in [32].…”

Section: 12-1 Generation Of Random Rough Surface Profile and Its Mmentioning

confidence: 99%

“…Most recently, a Monte-Carlo MPSTD algorithm has been developed for investigating the scattering of a cylinder buried below a random rough surface [32]. But the investigation was limited to a 2D configuration.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this paper, the 2D Monte-Carlo MPSTD algorithm presented in the authors' previous work [32] is further developed for studying the scattering of 3D objects buried below a random rough surface. The rest of this paper is organized as the follows.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Scattering From Three-Dimensional Objects Buried Below a Random Rough Surface by Monte-Carlo MPSTD Method

Liu¹,

Dai²,

Xu³

2013

PIER B

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Abstract-This paper presents a Monte-Carlo multidomain pseudospectral time-domain (MPSTD) algorithm developed for the analysis of scattering from a three dimensional (3D) object buried below a random rough surface separating two half spaces. In the development, special attention is paid to the 3D computation domain decomposition and subdomain mapping involving the random rough surface as well as the subdomain patching along the rough surface. The Mote-Carlo MPSTD algorithm is then employed to determine the scattering of 3D objects of various shapes and electromagnetic properties; embedded in the lower half space with different permittivity and the roughness of the random rough surface may vary. Sample numerical results are presented, validated, and analyzed. Through the analysis, it is observed that the roughness of the random rough surface and the electromagnetic properties of the lower half space can significantly affect the scattered signature of the buried object.

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Section: 12-1 Generation Of Random Rough Surface Profile and Its Mmentioning

confidence: 85%

Section: 12-1 Generation Of Random Rough Surface Profile and Its Mmentioning

confidence: 99%

Section: 12-1 Generation Of Random Rough Surface Profile and Its Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of Scattering From Three-Dimensional Objects Buried Below a Random Rough Surface by Monte-Carlo MPSTD Method

Liu¹,

Dai²,

Xu³

2013

PIER B

Self Cite

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Machine learning classification of metallic objects using pulse induction electromagnetic data

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents the classification of metallic objects using total and scattered pulse induction electromagnetic data, with a classification accuracy greater than 90%. Machine learning classification is applied to raw electromagnetic induction (EMI) data without the use of a physics-based model. The EMI method is applied to 8 metallic objects placed at increasing distances from 10–55 mm to the EMI sensing system. The EMI sensing system consists of two RL circuits placed in close proximity. Metallic objects are classified using linear algorithms including a perceptron and multiclass logistic regression, and nonlinear algorithms including a neural network,a 1D and 2D convolutional neural network (CNN). EMI data was collected using an experiment in an electromagnetically shielded laboratory. Feature maps are presented that explain the salient components of the EMI data used by the 1D and 2D CNN.

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A Monte-Carlo MPSTD Analysis of Scattering From Cylinders Buried Below a Random Periodic Rough Surface

Dai¹,

Liu²,

Xu³

2013

PIER B

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Abstract-The analysis of scattering of objects buried below a random rough surface is of practical interest. In reality, the random rough surface may be of an extensive periodic structure. To deal with this more realistic situation, this paper presents a Monte-Carlo MPSTD numerical technique developed for investigating the scattering of a cylinder buried below a random periodic rough surface. The computation model is formulated in two steps. In the first step, only the random rough surface is considered and the periodic boundary condition (PBC) is enforced at the two ends of a period of the rough surface. Then, in the second step, a cylinder is placed below the random rough surface and the interaction between the buried cylinder and the rough surface is taken into account. In each of the two steps, the fields are computed employing the MPSTD algorithm developed in the authors' previous work. Sample numerical results are presented and validated.

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Scattering of Object Buried below Random Rough Surface—A Monte Carlo Pseudospectral Time-Domain Approach

Cited by 3 publications

References 32 publications

Analysis of Scattering From Three-Dimensional Objects Buried Below a Random Rough Surface by Monte-Carlo MPSTD Method

Analysis of Scattering From Three-Dimensional Objects Buried Below a Random Rough Surface by Monte-Carlo MPSTD Method

Machine learning classification of metallic objects using pulse induction electromagnetic data

A Monte-Carlo MPSTD Analysis of Scattering From Cylinders Buried Below a Random Periodic Rough Surface

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