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Subspace averaging and order determination for source enumeration

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorGarg, Vaibhav
dc.contributor.authorSantamaría, Ignacio
dc.contributor.authorRamírez García, David
dc.contributor.authorScharf, Louis L.
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-12-02T10:31:35Z
dc.date.available2020-12-02T10:31:35Z
dc.date.issued2019-04-18
dc.descriptionThis paper was presented in part at the 2016 Workshop on Statistical Signal Processing, Palma de Mallorca, Spain, June 2016, and in part at the 2018 Workshop on Statistical Signal Processing, Freiburg, Germany, June 2018.en
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.en
dc.description.sponsorshipThe work of V. Garg and I. Santamaría 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
dc.description.statusPublicado
dc.format.extent13
dc.identifier.bibliographicCitationIEEE Transactions on Signal Processing, (2019), 67(11), pp.: 3028-3041.en
dc.identifier.doihttps://doi.org/10.1109/TSP.2019.2912151
dc.identifier.issn1053-587X
dc.identifier.publicationfirstpage3028
dc.identifier.publicationissue11
dc.identifier.publicationlastpage3041
dc.identifier.publicationtitleIEEE Transactions on Signal Processingen
dc.identifier.publicationvolume67
dc.identifier.urihttps://hdl.handle.net/10016/31509
dc.identifier.uxxiAR/0000025041
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TEC2016-75067-C4-4-R/CARMENes
dc.relation.projectIDGobierno de España. TEC2015-69648-REDCes
dc.relation.projectIDGobierno de España. BES-2017-080542es
dc.relation.projectIDGobierno de España. TEC2017-92552-EXP/aMBITIONes
dc.relation.projectIDGobierno de España. TEC2015-69868- C2-1-R/ADVENTUREes
dc.relation.projectIDGobierno de España. TEC2017-86921-C2-2-R/CAIMANes
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-4705/PRACTICOCMes
dc.rights© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.es
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherArray processingen
dc.subject.otherDimensionen
dc.subject.otherGrassmann manifolden
dc.subject.otherOrder estimationen
dc.subject.otherSource enumerationen
dc.subject.otherSubspace averagingen
dc.titleSubspace averaging and order determination for source enumerationen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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