An Accelerated Nonlinear Contrast Source Inversion Scheme for Sparse Electromagnetic Imaging
An efficient nonlinear contrast source inversion scheme for electromagnetic imaging of sparse two-dimensional investigation domains is proposed. To avoid generating a sequence of linear sparse optimization problems, the non-linearity is directly tackled using the nonlinear Landweber (NLW) iterations...
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| Veröffentlicht in: | IEEE access 2021, Vol.9, p.54811-54819 |
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| creator | Sandhu, Ali Imran Desmal, Abdulla Bagci, Hakan |
| description | An efficient nonlinear contrast source inversion scheme for electromagnetic imaging of sparse two-dimensional investigation domains is proposed. To avoid generating a sequence of linear sparse optimization problems, the non-linearity is directly tackled using the nonlinear Landweber (NLW) iterations. A self-adaptive projected accelerated steepest descent (A-PASD) algorithm is incorporated to enhance the efficiency of the NLW iterations. The algorithm enforces the sparsity constraint by projecting the result of each steepest descent iteration into the L_{1} -norm ball and selects the largest-possible iteration step without sacrificing from convergence. Numerical results, which demonstrate the proposed scheme's accuracy, efficiency, and applicability, are presented. |
| doi_str_mv | 10.1109/ACCESS.2021.3071397 |
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To avoid generating a sequence of linear sparse optimization problems, the non-linearity is directly tackled using the nonlinear Landweber (NLW) iterations. A self-adaptive projected accelerated steepest descent (A-PASD) algorithm is incorporated to enhance the efficiency of the NLW iterations. The algorithm enforces the sparsity constraint by projecting the result of each steepest descent iteration into the <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula>-norm ball and selects the largest-possible iteration step without sacrificing from convergence. Numerical results, which demonstrate the proposed scheme's accuracy, efficiency, and applicability, are presented.</description><subject>Adaptive algorithms</subject><subject>Approximation algorithms</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Contrast source inversion</subject><subject>Convergence</subject><subject>electromagnetic imaging</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Imaging</subject><subject>inverse problems</subject><subject>Iterative methods</subject><subject>Landweber iterations</subject><subject>Linearity</subject><subject>nonlinear inverse scattering</subject><subject>Nonlinearity</subject><subject>Optimization</subject><subject>Permittivity</subject><subject>Scattering</subject><subject>Science & Technology</subject><subject>sparse reconstruction</subject><subject>steepest descent algorithm</subject><subject>Technology</subject><subject>Telecommunications</subject><subject>Two dimensional displays</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>HGBXW</sourceid><sourceid>DOA</sourceid><recordid>eNqNkU1rGzEQhpfSQEOaX5CLoMdiR9Ks9uNoFrc1hPawza0gZrUjd81aciW5pf--cjakPVYgNAzPOzOatyjuBF8Lwdv7Tddt-34tuRRr4LWAtn5VXEtRtStQUL3-J35T3MZ44Pk0OaXq6-LbxrGNMTRTwEQj--zdPDnCwDrvUsCYWO_PwRDbuZ8U4uQd6813OhKzPrD-hCES285kUvBH3DtKk2G7HE1u_7a4sjhHun1-b4rHD9uv3afVw5ePu27zsDIlb9JKGKvAkq2VGlCMQzkAyBIaSa2s6qGq0EqbSUCZrwDksmzkYAkb0RrL4abYLXVHjwd9CtMRw2_tcdJPCR_2GkOeayZNAkZrZEsksaxH2yAqVY8IZTVSwynXerfUOgX_40wx6UP-v8vja6kEQAulrDIFC2WCjzGQfekquL64ohdX9MUV_exKVjWL6hcN3kYzkTP0osyuVKXgXMHFINFNCVNed-fPLmXp-_-XZvpuoSeiv1QLraryYv8ASbmpaQ</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Sandhu, Ali Imran</creator><creator>Desmal, Abdulla</creator><creator>Bagci, Hakan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8760-8374</orcidid><orcidid>https://orcid.org/0000-0003-3867-5786</orcidid><orcidid>https://orcid.org/0000-0003-0861-8908</orcidid></search><sort><creationdate>2021</creationdate><title>An Accelerated Nonlinear Contrast Source Inversion Scheme for Sparse Electromagnetic Imaging</title><author>Sandhu, Ali Imran ; Desmal, Abdulla ; Bagci, Hakan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-1cf53fef755ba1db4b3324382e9267b66af2f4083a283a13a02482bfea819cf03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive algorithms</topic><topic>Approximation algorithms</topic><topic>Computer Science</topic><topic>Computer Science, Information Systems</topic><topic>Contrast source inversion</topic><topic>Convergence</topic><topic>electromagnetic imaging</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>Imaging</topic><topic>inverse problems</topic><topic>Iterative methods</topic><topic>Landweber iterations</topic><topic>Linearity</topic><topic>nonlinear inverse scattering</topic><topic>Nonlinearity</topic><topic>Optimization</topic><topic>Permittivity</topic><topic>Scattering</topic><topic>Science & Technology</topic><topic>sparse reconstruction</topic><topic>steepest descent algorithm</topic><topic>Technology</topic><topic>Telecommunications</topic><topic>Two dimensional displays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sandhu, Ali Imran</creatorcontrib><creatorcontrib>Desmal, Abdulla</creatorcontrib><creatorcontrib>Bagci, Hakan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sandhu, Ali Imran</au><au>Desmal, Abdulla</au><au>Bagci, Hakan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Accelerated Nonlinear Contrast Source Inversion Scheme for Sparse Electromagnetic Imaging</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><stitle>IEEE ACCESS</stitle><date>2021</date><risdate>2021</risdate><volume>9</volume><spage>54811</spage><epage>54819</epage><pages>54811-54819</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>An efficient nonlinear contrast source inversion scheme for electromagnetic imaging of sparse two-dimensional investigation domains is proposed. To avoid generating a sequence of linear sparse optimization problems, the non-linearity is directly tackled using the nonlinear Landweber (NLW) iterations. A self-adaptive projected accelerated steepest descent (A-PASD) algorithm is incorporated to enhance the efficiency of the NLW iterations. The algorithm enforces the sparsity constraint by projecting the result of each steepest descent iteration into the <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula>-norm ball and selects the largest-possible iteration step without sacrificing from convergence. 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| subjects | Adaptive algorithms Approximation algorithms Computer Science Computer Science, Information Systems Contrast source inversion Convergence electromagnetic imaging Engineering Engineering, Electrical & Electronic Imaging inverse problems Iterative methods Landweber iterations Linearity nonlinear inverse scattering Nonlinearity Optimization Permittivity Scattering Science & Technology sparse reconstruction steepest descent algorithm Technology Telecommunications Two dimensional displays |
| title | An Accelerated Nonlinear Contrast Source Inversion Scheme for Sparse Electromagnetic Imaging |
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