DOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunity

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dc.contributor.authorHu, Boen
dc.contributor.authorLiu, Mingqianen
dc.contributor.authorYi, Feien
dc.contributor.authorSong, Haoen
dc.contributor.authorJiang, Fanen
dc.contributor.authorGong, Fengkuien
dc.contributor.authorZhao, Nanen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2020-05-28T13:26:44Zen
dc.date.available2020-05-28T13:26:44Zen
dc.date.issued2020-01-14en
dc.description.abstractTraditional DOA estimation algorithms have poor adaptability to antenna errors. To enhance the direction of arrival (DOA) estimation performance for moving target echo signals in the environment of multiple type illuminators of opportunity, a DOA estimation framework leveraging deep learning networks (DLN) is proposed. In the proposed framework, the DLN is divided into two main components, including linear classification networks (LCN) and convolutional neural networks (CCN). The LCN is utilized to identify the spatial subregion of received signals and divide the signals from each subregion into corresponding output modules. Then, the output of the LCN after matrix transformations will be input into multiple parallel CNNs, where DOA estimations are carried out. Extensive simulation studies are conducted, demonstrating that our proposed method has excellent estimation performance and strong universality with high estimation accuracy even under large antenna defects.en
dc.description.notesThis work was supported in part by the National Natural Science Foundation of China under Grant 61501348, Grant 61801363, and Grant 61871065, in part by the Shaanxi Provincial Key Research and Development Program under Grant 2019GY-043, in part by the Open Research Fund of Shaanxi Key Laboratory of Information Communication Network and Security under Grant ICNS201703, in part by the China Postdoctoral Science Foundation under Grant 2017M611912, in part by the Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 1701059B, in part by the 111 Project under Grant B08038, and in part by the China Scholarship Council under Grant 201806965031.en
dc.description.sponsorshipNational Natural Science Foundation of ChinaNational Natural Science Foundation of China [61501348, 61801363, 61871065]; Shaanxi Provincial Key Research and Development Program [2019GY-043]; Shaanxi Key Laboratory of Information Communication Network and Security [ICNS201703]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M611912]; Jiangsu Planned Projects for Postdoctoral Research FundsJiangsu Planned Projects for Postdoctoral Research Funds [1701059B]; 111 ProjectMinistry of Education, China - 111 Project [B08038]; China Scholarship CouncilChina Scholarship Council [201806965031]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2020.2966653en
dc.identifier.issn2169-3536en
dc.identifier.urihttp://hdl.handle.net/10919/98579en
dc.identifier.volume8en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectConvolutional neural networksen
dc.subjectdeep learning networksen
dc.subjectdirection of arrival estimationen
dc.subjectilluminator of opportunityen
dc.subjectlinear classification networksen
dc.titleDOA Robust Estimation of Echo Signals Based on Deep Learning Networks With Multiple Type Illuminators of Opportunityen
dc.title.serialIEEE Accessen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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