Cognitive radar antenna selection via deep learning

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dc.contributor.authorElbir, Ahmet Musab
dc.contributor.authorMishra, Kumar Vijay
dc.contributor.authorEldar, Yonina
dc.date.accessioned2020-04-30T22:40:57Z
dc.date.available2020-04-30T22:40:57Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionElbir, Ahmet M./0000-0003-4060-3781en_US
dc.descriptionWOS: 000498816400002en_US
dc.description.abstractDirection-of-arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is a recent interest in thinning the antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the antenna selection problem. Specifically, they construct a convolutional neural network (CNN) as a multi-class classification framework, where each class designates a different subarray. The proposed network determines a new array every time data is received by the radar, thereby making antenna selection a cognitive operation. Their numerical experiments show that the proposed CNN structure provides 22% better classification performance than a support vector machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.en_US
dc.description.sponsorshipEuropean UnionEuropean Union (EU) [646804-ERC-COG-BNYQ]; Andrew and Erna Finci Viterbi Fellowship; Lady Davis Fellowshipen_US
dc.description.sponsorshipK.V.M. and Y.C.E. were funded from the European Union's Horizon 2020 research and innovation programme under Grant agreement no. 646804-ERC-COG-BNYQ. K.V.M. also acknowledges partial support via Andrew and Erna Finci Viterbi Fellowship and Lady Davis Fellowship.en_US
dc.identifier.doi10.1049/iet-rsn.2018.5438en_US
dc.identifier.endpage880en_US
dc.identifier.issn1751-8784
dc.identifier.issn1751-8792
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage871en_US
dc.identifier.urihttps://doi.org/10.1049/iet-rsn.2018.5438
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3092
dc.identifier.volume13en_US
dc.identifier.wosWOS:000498816400002en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInst Engineering Technology-Ieten_US
dc.relation.ispartofIet Radar Sonar And Navigationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleCognitive radar antenna selection via deep learningen_US
dc.typeArticleen_US

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