Regularized minimal residual method for permittivity reconstruction in microwave imaging
In this paper, a regularized reconstruction based on the minimal residual method is proposed for microwave imaging applications. The method provides optimum regularization parameter to estimate the distribution of permittivity values of unknown scatterers under test. Initially, the method is applied...
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| Veröffentlicht in: | Microwave and optical technology letters 2020-12, Vol.62 (12), p.3682-3694 |
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| container_title | Microwave and optical technology letters |
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| creator | Magdum, Amit Erramshetty, Mallikarjun Jagannath, Ravi Prasad K. |
| description | In this paper, a regularized reconstruction based on the minimal residual method is proposed for microwave imaging applications. The method provides optimum regularization parameter to estimate the distribution of permittivity values of unknown scatterers under test. Initially, the method is applied to Born approximated linear model for weak scatterers. The performance of this approach is compared with the commonly adopted Morozov's discrepancy principle used in conjunction with the Tikhonov regularization. The effectiveness of the method is assisted by simulating the various numerical examples of synthetic and experimental data. The results of numerical simulations validate that the proposed method is highly effective. Thereafter, a non‐linear inverse problem based on the inexact Newton method is examined for the estimation of strong scatterers. Here also, the proposed method is found to be helpful in terms of improving the image accuracy. |
| doi_str_mv | 10.1002/mop.32487 |
| format | Article |
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The method provides optimum regularization parameter to estimate the distribution of permittivity values of unknown scatterers under test. Initially, the method is applied to Born approximated linear model for weak scatterers. The performance of this approach is compared with the commonly adopted Morozov's discrepancy principle used in conjunction with the Tikhonov regularization. The effectiveness of the method is assisted by simulating the various numerical examples of synthetic and experimental data. The results of numerical simulations validate that the proposed method is highly effective. Thereafter, a non‐linear inverse problem based on the inexact Newton method is examined for the estimation of strong scatterers. 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The method provides optimum regularization parameter to estimate the distribution of permittivity values of unknown scatterers under test. Initially, the method is applied to Born approximated linear model for weak scatterers. The performance of this approach is compared with the commonly adopted Morozov's discrepancy principle used in conjunction with the Tikhonov regularization. The effectiveness of the method is assisted by simulating the various numerical examples of synthetic and experimental data. The results of numerical simulations validate that the proposed method is highly effective. Thereafter, a non‐linear inverse problem based on the inexact Newton method is examined for the estimation of strong scatterers. Here also, the proposed method is found to be helpful in terms of improving the image accuracy.</description><subject>Born approximation</subject><subject>Computer simulation</subject><subject>Image reconstruction</subject><subject>Inverse problems</subject><subject>microwave imaging</subject><subject>minimal residual method</subject><subject>Newton methods</subject><subject>Parameter estimation</subject><subject>Permittivity</subject><subject>Regularization</subject><issn>0895-2477</issn><issn>1098-2760</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kEtPwzAQhC0EEqVw4B9E4sQhrV-xnSOqeElFRQgkblYednGVxMF2WoVfjyFcuezu4ZvZ0QBwieACQYiXre0XBFPBj8AMwVykmDN4DGZQ5FmKKeen4Mz7HYSQcI5n4P1FbYemcOZL1UlrOtMWTeKUN_UQj1aFD1sn2rqkV641IZi9CWMEKtv54IYqGNslpovSytlDsVdJdNiabnsOTnTReHXxt-fg7e72dfWQrjf3j6ubdVrhnPM4S16wTHFRiwwTqGtBiKIZoVhlXJQ617hEnJKMZQzlZVEzTYioCIoXJxWZg6vJt3f2c1A-yJ0dXBdfSkypYDlnGEbqeqJiSu-d0rJ3MagbJYLypzgZi5O_xUV2ObEH06jxf1A-bZ4nxTfO3HCf</recordid><startdate>202012</startdate><enddate>202012</enddate><creator>Magdum, Amit</creator><creator>Erramshetty, Mallikarjun</creator><creator>Jagannath, Ravi Prasad K.</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-1494-4382</orcidid></search><sort><creationdate>202012</creationdate><title>Regularized minimal residual method for permittivity reconstruction in microwave imaging</title><author>Magdum, Amit ; Erramshetty, Mallikarjun ; Jagannath, Ravi Prasad K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2977-c2b7a65e78d85230fd833e45342e578bf9f2b1743565619bad6f338c31ad673c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Born approximation</topic><topic>Computer simulation</topic><topic>Image reconstruction</topic><topic>Inverse problems</topic><topic>microwave imaging</topic><topic>minimal residual method</topic><topic>Newton methods</topic><topic>Parameter estimation</topic><topic>Permittivity</topic><topic>Regularization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Magdum, Amit</creatorcontrib><creatorcontrib>Erramshetty, Mallikarjun</creatorcontrib><creatorcontrib>Jagannath, Ravi Prasad K.</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Microwave and optical technology letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Magdum, Amit</au><au>Erramshetty, Mallikarjun</au><au>Jagannath, Ravi Prasad K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Regularized minimal residual method for permittivity reconstruction in microwave imaging</atitle><jtitle>Microwave and optical technology letters</jtitle><date>2020-12</date><risdate>2020</risdate><volume>62</volume><issue>12</issue><spage>3682</spage><epage>3694</epage><pages>3682-3694</pages><issn>0895-2477</issn><eissn>1098-2760</eissn><abstract>In this paper, a regularized reconstruction based on the minimal residual method is proposed for microwave imaging applications. The method provides optimum regularization parameter to estimate the distribution of permittivity values of unknown scatterers under test. Initially, the method is applied to Born approximated linear model for weak scatterers. The performance of this approach is compared with the commonly adopted Morozov's discrepancy principle used in conjunction with the Tikhonov regularization. The effectiveness of the method is assisted by simulating the various numerical examples of synthetic and experimental data. The results of numerical simulations validate that the proposed method is highly effective. Thereafter, a non‐linear inverse problem based on the inexact Newton method is examined for the estimation of strong scatterers. 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| subjects | Born approximation Computer simulation Image reconstruction Inverse problems microwave imaging minimal residual method Newton methods Parameter estimation Permittivity Regularization |
| title | Regularized minimal residual method for permittivity reconstruction in microwave imaging |
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