The linear sampling method for random sources
We present an extension of the linear sampling method for solving the sound-soft inverse acoustic scattering problem with randomly distributed point sources. The theoretical justification of our sampling method is based on the Helmholtz--Kirchhoff identity, the cross-correlation between measurements...
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| creator | Garnier, Josselin Haddar, Houssem Montanelli, Hadrien |
| description | We present an extension of the linear sampling method for solving the
sound-soft inverse acoustic scattering problem with randomly distributed point
sources. The theoretical justification of our sampling method is based on the
Helmholtz--Kirchhoff identity, the cross-correlation between measurements, and
the volume and imaginary near-field operators, which we introduce and analyze.
Implementations in MATLAB using boundary elements, the SVD, Tikhonov
regularization, and Morozov's discrepancy principle are also discussed. We
demonstrate the robustness and accuracy of our algorithms with several
numerical experiments in two dimensions. |
| doi_str_mv | 10.48550/arxiv.2210.15560 |
| format | Article |
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sound-soft inverse acoustic scattering problem with randomly distributed point
sources. The theoretical justification of our sampling method is based on the
Helmholtz--Kirchhoff identity, the cross-correlation between measurements, and
the volume and imaginary near-field operators, which we introduce and analyze.
Implementations in MATLAB using boundary elements, the SVD, Tikhonov
regularization, and Morozov's discrepancy principle are also discussed. We
demonstrate the robustness and accuracy of our algorithms with several
numerical experiments in two dimensions.</description><identifier>DOI: 10.48550/arxiv.2210.15560</identifier><language>eng</language><subject>Computer Science - Numerical Analysis ; Mathematics - Numerical Analysis</subject><creationdate>2022-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2210.15560$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2210.15560$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Garnier, Josselin</creatorcontrib><creatorcontrib>Haddar, Houssem</creatorcontrib><creatorcontrib>Montanelli, Hadrien</creatorcontrib><title>The linear sampling method for random sources</title><description>We present an extension of the linear sampling method for solving the
sound-soft inverse acoustic scattering problem with randomly distributed point
sources. The theoretical justification of our sampling method is based on the
Helmholtz--Kirchhoff identity, the cross-correlation between measurements, and
the volume and imaginary near-field operators, which we introduce and analyze.
Implementations in MATLAB using boundary elements, the SVD, Tikhonov
regularization, and Morozov's discrepancy principle are also discussed. We
demonstrate the robustness and accuracy of our algorithms with several
numerical experiments in two dimensions.</description><subject>Computer Science - Numerical Analysis</subject><subject>Mathematics - Numerical Analysis</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrsKwjAYBeAsDqI-gJN5geqfeztK8QaCS_eSNIkW2kZSFX1762U6hzMcPoTmBJY8FQJWOj7rx5LSYSBCSBijpLg43NSd0xH3ur0O9Yxbd7sEi32IOOrOhhb34R4r10_RyOumd7N_TlCx3RT5Pjmedod8fUy0VJBwnXGWZhklzFfeEMGJsd4rT6RPrSSKMyYsp1RVhskMBAAFa4RMwdmKGjZBi9_t11teY93q-Co_7vLrZm8Y_TwO</recordid><startdate>20221027</startdate><enddate>20221027</enddate><creator>Garnier, Josselin</creator><creator>Haddar, Houssem</creator><creator>Montanelli, Hadrien</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20221027</creationdate><title>The linear sampling method for random sources</title><author>Garnier, Josselin ; Haddar, Houssem ; Montanelli, Hadrien</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-4a943899213fcfb1541bdff7f16f8d6174335d4227cb369050020db5680edc2b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Numerical Analysis</topic><topic>Mathematics - Numerical Analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Garnier, Josselin</creatorcontrib><creatorcontrib>Haddar, Houssem</creatorcontrib><creatorcontrib>Montanelli, Hadrien</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Garnier, Josselin</au><au>Haddar, Houssem</au><au>Montanelli, Hadrien</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The linear sampling method for random sources</atitle><date>2022-10-27</date><risdate>2022</risdate><abstract>We present an extension of the linear sampling method for solving the
sound-soft inverse acoustic scattering problem with randomly distributed point
sources. The theoretical justification of our sampling method is based on the
Helmholtz--Kirchhoff identity, the cross-correlation between measurements, and
the volume and imaginary near-field operators, which we introduce and analyze.
Implementations in MATLAB using boundary elements, the SVD, Tikhonov
regularization, and Morozov's discrepancy principle are also discussed. We
demonstrate the robustness and accuracy of our algorithms with several
numerical experiments in two dimensions.</abstract><doi>10.48550/arxiv.2210.15560</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Numerical Analysis Mathematics - Numerical Analysis |
| title | The linear sampling method for random sources |
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