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dc.contributor.authorGarg, Vaibhav
dc.contributor.authorSantamaría Caballero, Luis Ignacio 
dc.contributor.authorRamírez García, David
dc.contributor.authorScharf, Louis L. 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2020-01-30T11:59:57Z
dc.date.available2020-01-30T11:59:57Z
dc.date.issued2019-06-01
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.otherTEC2016-75067-C4-4-Res_ES
dc.identifier.otherTEC2015-69648-REDCes_ES
dc.identifier.otherTEC2017-92552-EXPes_ES
dc.identifier.otherTEC2015-69868-C2-1-Res_ES
dc.identifier.otherTEC2017-86921-C2-2-Res_ES
dc.identifier.urihttp://hdl.handle.net/10902/17953
dc.description.abstractIn this paper, we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.es_ES
dc.description.sponsorshipThe associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yuejie Chi. The work of V. Garg and I. Santamaria was supported in part by the Ministerio de Economía y Competitividad (MINECO) of Spain, and in part by the AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN), TEC2015-69648-REDC, and BES-2017-080542. The work of D. Ramírez was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant TEC2017-92552-EXP (aMBITION), in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grants TEC2015-69868-C2-1-R (ADVENTURE) and TEC2017-86921-C2-2-R (CAIMAN), and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICOCM). The work of L. L. Scharf was supported in part by the U.S. NSF under Contract CISE-1712788.es_ES
dc.format.extent14 p.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.es_ES
dc.sourceIEEE Transactions on Signal Processing, 2019, 67(11), 3028-3041es_ES
dc.subject.otherArray processinges_ES
dc.subject.otherDimensiones_ES
dc.subject.otherGrassmann manifoldes_ES
dc.subject.otherOrder estimationes_ES
dc.subject.otherSource enumerationes_ES
dc.subject.otherSubspace averaginges_ES
dc.titleSubspace averaging and order determination for source enumerationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1109/TSP.2019.2912151es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1109/TSP.2019.2912151
dc.type.versionacceptedVersiones_ES


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