Norm Optimization using Machine Learning Approach for Autofocus in mmWave SAR Imaging

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dc.contributor.authorKim, Jin-Wooko
dc.contributor.authorKim, Jeong-Wookko
dc.contributor.authorKim, Solko
dc.contributor.authorKim, Ghooko
dc.contributor.authorYu, Jong-Wonko
dc.date.accessioned2022-11-25T03:01:38Z-
dc.date.available2022-11-25T03:01:38Z-
dc.date.created2022-11-24-
dc.date.issued2019-07-12-
dc.identifier.citation2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019, pp.97 - 98-
dc.identifier.urihttp://hdl.handle.net/10203/300973-
dc.description.abstractDue to the shorter wavelength of mm-wave SAR for higher resolution, high-frequency phase error (HPE) can be generated even by small vibration of antenna phase center and distortion to SAR image becomes significant. For the problem, the machine learning approach can be utilized in SAR autofocus by classifying images and optimizing the objective function for autofocus. A hybrid form of L1/ L2-norm is adapted to the range compressed data corresponding to the input of the autofocus taking advantage of the convergence speed and the stability. Its convergence feature is analyzed and demonstrated in the simulation.-
dc.languageEnglish-
dc.publisher2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019-
dc.titleNorm Optimization using Machine Learning Approach for Autofocus in mmWave SAR Imaging-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.beginningpage97-
dc.citation.endingpage98-
dc.citation.publicationname2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationAtlanta, Georgia-
dc.contributor.localauthorYu, Jong-Won-
dc.contributor.nonIdAuthorKim, Jin-Woo-
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EE-Conference Papers(학술회의논문)
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