Joint-block-sparsity for efficient 2-D DOA estimation with multiple separable observations

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dc.contributor.authorElbir, Ahmet Musab
dc.date.accessioned2020-04-30T23:18:48Z
dc.date.available2020-04-30T23:18:48Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionElbir, Ahmet M./0000-0003-4060-3781en_US
dc.descriptionWOS: 000485972700002en_US
dc.description.abstractIn sparsity-based optimization problems, one of the major issue is computational complexity, especially when the unknown signal is represented in multi-dimensions such as in the problem of 2-D (azimuth and elevation) direction-of-arrival (DOA) estimation. In order to cope with this issue, this paper introduces a new sparsity structure that can be used to model the optimization problem in case of multiple data snapshots and multiple separable observations where the dictionary can be decomposed into two parts: azimuth and elevation dictionaries. The proposed sparsity structure is called joint-block-sparsity which enforces the sparsity in multiple dimensions, namely azimuth, elevation and data snapshots. In order to model the joint-block-sparsity in the optimization problem, triple mixed norms are used. In the simulations, the proposed method is compared with both sparsity-based techniques and subspace-based methods as well as the Cramer-Rao lower bound. It is shown that the proposed method effectively solves the 2-D DOA estimation problem with significantly low complexity and sufficient accuracy.en_US
dc.identifier.doi10.1007/s11045-018-0623-zen_US
dc.identifier.endpage1669en_US
dc.identifier.issn0923-6082
dc.identifier.issn1573-0824
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1659en_US
dc.identifier.urihttps://doi.org/10.1007/s11045-018-0623-z
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3531
dc.identifier.volume30en_US
dc.identifier.wosWOS:000485972700002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultidimensional Systems And Signal Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDirection of arrival estimationen_US
dc.subjectJoint-block-sparsityen_US
dc.subjectSeparable observationsen_US
dc.subjectTriple mixed normsen_US
dc.titleJoint-block-sparsity for efficient 2-D DOA estimation with multiple separable observationsen_US
dc.typeArticleen_US

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