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Application of Machine Learning to Synthesis of Maximally Sparse Linear Arrays
Alternative TitleWOS:000550769302147
Zhao, Xiaowen; Yang, Qingshan; Zhang, Yunhua1
Source Publication2019 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM - SPRING (PIERS-SPRING)
2019
Pages2917-2921
Language英语
ISSN1559-9450
ISBN978-1-7281-3403-1
AbstractIn this paper, an innovative method based on machine learning (ML) is proposed for synthesizing the maximally sparse linear arrays with focused and/or shaped patterns. The synthesis problem can be formulated as a ridge regression model with l(2)-regularization by jointing sparse signal recovery and dictionary parameter learning. Towards this end, the element excitations and positions are determined by solving the synthesis formulation with an alternative optimization strategy. Numerical simulations are conducted to validate the effectiveness and flexibility of the proposed method.
Conference NamePhotonIcs and Electromagnetics Research Symposium - Spring (PIERS-Spring)
Conference DateJUN 17-20, 2019
Conference PlaceRome, ITALY
Indexed ByCPCI
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7776
Collection中国科学院国家空间科学中心
Affiliation1.Chinese Acad Sci, Key Lab Microwave Remote Sensing, Natl Space Sci Ctr, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Xiaowen,Yang, Qingshan,Zhang, Yunhua. Application of Machine Learning to Synthesis of Maximally Sparse Linear Arrays[C],2019:2917-2921.
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