Deep-Sparse Array Cognitive Radar

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dc.contributor.authorElbir, Ahmet Musab
dc.contributor.authorMulleti, Satish
dc.contributor.authorCohen, Regev
dc.contributor.authorFu, Rong
dc.contributor.authorEldar, Yonina C.
dc.date.accessioned2020-04-30T13:32:16Z
dc.date.available2020-04-30T13:32:16Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description13th International Conference on Sampling Theory and Applications, SampTA 2019 -- 8 July 2019 through 12 July 2019 -- 158420en_US
dc.description.abstractIn antenna array based radar applications, it is often desirable to choose an optimum subarray from a full array to achieve a balance between hardware cost and resolution. Moreover, in a cognitive radar system, the sparse subarrays are chosen based on the target scenario at that instant. Recently, a deep-learning based antenna selection technique was proposed for a single target scenario. In this paper, we extend this approach to multiple targets and assess the performance of state-of-the-art direction of arrival estimation techniques in conjunction with the proposed antenna selection method. To optimally choose the subarrays based on the target DOAs, we design a convolutional neural network which accepts the array covariance matrix as an input and selects the best sparse subarray that minimizes the error. Once the optimum sparse subarray is obtained, the signals from the selected antennas are used to estimate the DOAs. We provide numerical simulations to validate the performance of the proposed cognitive array selection strategy. We show that the proposed approach outperforms random sparse antenna selection and it leads to a higher DOA estimation accuracy by 6 dB. © 2019 IEEE.en_US
dc.description.sponsorshipAir Force Office of Scientific Researchen_US
dc.description.sponsorship—————————- This project has received funding from the Air Force Office of Scientific Research under grant No. FA9550-18-1-0208.en_US
dc.identifier.doi10.1109/SampTA45681.2019.9030833en_US
dc.identifier.isbn9781728137414
dc.identifier.urihttps://dx.doi.org/10.1109/SampTA45681.2019.9030833
dc.identifier.urihttps://hdl.handle.net/20.500.12684/204
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2019 13th International Conference on Sampling Theory and Applications, SampTA 2019en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleDeep-Sparse Array Cognitive Radaren_US
dc.typeConference Objecten_US

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