Postprocessing of the linear sampling method in inverse electromagnetic scattering problem for obstacles

Lei Liu; Weinong Fu; Shuangxia Niu

doi:10.1109/CEFC.2016.7816161

Postprocessing of the linear sampling method in inverse electromagnetic scattering problem for obstacles

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

The linear sampling method is known to be a simple and computationally efficient approach to retrieve the support of the scatterer using multistatic scattered field data. However, the recovered profile is always misleading, owing to the lack of robust edge detecting. This paper addresses this open issue. Using moving least square approximation, the upper and lower bounds of the profile of scatterers are pursued, and a sweeping process finds the optimal profile to match the scattered field data.

Original language	English
Title of host publication	IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation
Publisher	IEEE
ISBN (Electronic)	9781509010325
DOIs	https://doi.org/10.1109/CEFC.2016.7816161
Publication status	Published - 12 Jan 2017
Event	17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016 - Hotel Hilton Miami Downtown, Miami, United States Duration: 13 Nov 2016 → 16 Nov 2016

Conference

Conference	17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016
Country	United States
City	Miami
Period	13/11/16 → 16/11/16

Keywords

Curvature
Inverse scattering
Linear sampling method
Moving least square method

ASJC Scopus subject areas

Computational Mathematics
Instrumentation
Electrical and Electronic Engineering

More information

10.1109/CEFC.2016.7816161

Cite this

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title = "Postprocessing of the linear sampling method in inverse electromagnetic scattering problem for obstacles",

abstract = "The linear sampling method is known to be a simple and computationally efficient approach to retrieve the support of the scatterer using multistatic scattered field data. However, the recovered profile is always misleading, owing to the lack of robust edge detecting. This paper addresses this open issue. Using moving least square approximation, the upper and lower bounds of the profile of scatterers are pursued, and a sweeping process finds the optimal profile to match the scattered field data.",

keywords = "Curvature, Inverse scattering, Linear sampling method, Moving least square method",

author = "Lei Liu and Weinong Fu and Shuangxia Niu",

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Liu, L, Fu, W & Niu, S 2017, Postprocessing of the linear sampling method in inverse electromagnetic scattering problem for obstacles. in IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation., 7816161, IEEE, 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016, Miami, United States, 13/11/16. https://doi.org/10.1109/CEFC.2016.7816161

Postprocessing of the linear sampling method in inverse electromagnetic scattering problem for obstacles. / Liu, Lei; Fu, Weinong ; Niu, Shuangxia.

IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation. IEEE, 2017. 7816161.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - The linear sampling method is known to be a simple and computationally efficient approach to retrieve the support of the scatterer using multistatic scattered field data. However, the recovered profile is always misleading, owing to the lack of robust edge detecting. This paper addresses this open issue. Using moving least square approximation, the upper and lower bounds of the profile of scatterers are pursued, and a sweeping process finds the optimal profile to match the scattered field data.

AB - The linear sampling method is known to be a simple and computationally efficient approach to retrieve the support of the scatterer using multistatic scattered field data. However, the recovered profile is always misleading, owing to the lack of robust edge detecting. This paper addresses this open issue. Using moving least square approximation, the upper and lower bounds of the profile of scatterers are pursued, and a sweeping process finds the optimal profile to match the scattered field data.

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KW - Inverse scattering

KW - Linear sampling method

KW - Moving least square method

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M3 - Conference article published in proceeding or book

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PB - IEEE

T2 - 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016

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Postprocessing of the linear sampling method in inverse electromagnetic scattering problem for obstacles

Abstract

Conference

Keywords

ASJC Scopus subject areas

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