Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method

Radar imaging using multiple input multiple output systems are becoming popular recently. These applications typically contain a sparse scene and the imaging system is challenged by the requirement of high quality real-time image reconstruction from under-sampled measurements via compressive sensing...

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Autores principales:	Emre A. Miran, Figen S. Oktem, Sencer Koc
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Multiple-input-multiple-output radar imaging near-field imaging inverse problem sparse reconstruction fast multipole method Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/aeb8664ecda542c481c811d585582744
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id	oai:doaj.org-article:aeb8664ecda542c481c811d585582744
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spelling	oai:doaj.org-article:aeb8664ecda542c481c811d5855827442021-11-17T00:00:35ZSparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method2169-353610.1109/ACCESS.2021.3126472https://doaj.org/article/aeb8664ecda542c481c811d5855827442021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606700/https://doaj.org/toc/2169-3536Radar imaging using multiple input multiple output systems are becoming popular recently. These applications typically contain a sparse scene and the imaging system is challenged by the requirement of high quality real-time image reconstruction from under-sampled measurements via compressive sensing. In this paper, we deal with obtaining sparse solution to near- field radar imaging problems by developing efficient sparse reconstruction, which avoid storing and using large-scale sensing matrices. We demonstrate that the “fast multipole method” can be employed within sparse reconstruction algorithms to efficiently compute the sensing operator and its adjoint (backward) operator, hence improving the computation speed and memory usage, especially for large-scale 3-D imaging problems. For several near-field imaging scenarios including point scatterers and 2-D/3-D extended targets, the performances of sparse reconstruction algorithms are numerically tested in comparison with a classical solver. Furthermore, effectiveness of the fast multipole method and efficient reconstruction are illustrated in terms of memory requirement and processing time.Emre A. MiranFigen S. OktemSencer KocIEEEarticleMultiple-input-multiple-output radar imagingnear-field imaginginverse problemsparse reconstructionfast multipole methodElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151578-151589 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multiple-input-multiple-output radar imaging near-field imaging inverse problem sparse reconstruction fast multipole method Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Multiple-input-multiple-output radar imaging near-field imaging inverse problem sparse reconstruction fast multipole method Electrical engineering. Electronics. Nuclear engineering TK1-9971 Emre A. Miran Figen S. Oktem Sencer Koc Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
description	Radar imaging using multiple input multiple output systems are becoming popular recently. These applications typically contain a sparse scene and the imaging system is challenged by the requirement of high quality real-time image reconstruction from under-sampled measurements via compressive sensing. In this paper, we deal with obtaining sparse solution to near- field radar imaging problems by developing efficient sparse reconstruction, which avoid storing and using large-scale sensing matrices. We demonstrate that the “fast multipole method” can be employed within sparse reconstruction algorithms to efficiently compute the sensing operator and its adjoint (backward) operator, hence improving the computation speed and memory usage, especially for large-scale 3-D imaging problems. For several near-field imaging scenarios including point scatterers and 2-D/3-D extended targets, the performances of sparse reconstruction algorithms are numerically tested in comparison with a classical solver. Furthermore, effectiveness of the fast multipole method and efficient reconstruction are illustrated in terms of memory requirement and processing time.
format	article
author	Emre A. Miran Figen S. Oktem Sencer Koc
author_facet	Emre A. Miran Figen S. Oktem Sencer Koc
author_sort	Emre A. Miran
title	Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
title_short	Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
title_full	Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
title_fullStr	Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
title_full_unstemmed	Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method
title_sort	sparse reconstruction for near-field mimo radar imaging using fast multipole method
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/aeb8664ecda542c481c811d585582744
work_keys_str_mv	AT emreamiran sparsereconstructionfornearfieldmimoradarimagingusingfastmultipolemethod AT figensoktem sparsereconstructionfornearfieldmimoradarimagingusingfastmultipolemethod AT sencerkoc sparsereconstructionfornearfieldmimoradarimagingusingfastmultipolemethod
_version_	1718426067957448704

Sparse Reconstruction for Near-Field MIMO Radar Imaging Using Fast Multipole Method

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