{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-linear-method-for-shape-reconstruction","title":"A Linear Method for Shape Reconstruction based on the Generalized Multiple Measurement Vectors Model","arxiv_id":"1906.10875","date":"2019-06-26","proceeding":null,"authors":[],"abstract":"In this paper, a novel linear method for shape reconstruction is proposed\nbased on the generalized multiple measurement vectors (GMMV) model. Finite\ndifference frequency domain (FDFD) is applied to discretized Maxwell's\nequations, and the contrast sources are solved iteratively by exploiting the\njoint sparsity as a regularized constraint. Cross validation (CV) technique is\nused to terminate the iterations, such that the required estimation of the\nnoise level is circumvented. The validity is demonstrated with an excitation of\ntransverse magnetic (TM) experimental data, and it is observed that, in the\naspect of focusing performance, the GMMV-based linear method outperforms the\nextensively used linear sampling method (LSM).","url_abs":"http://arxiv.org/abs/1906.10875v1","url_pdf":"http://arxiv.org/pdf/1906.10875v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-linear-method-for-shape-reconstruction","repo_url":"https://github.com/TUDsun/GMMV-LIM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}