Subspace averaging and order determination for source enumeration

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dc.contributor.author Garg, Vaibhav
dc.contributor.author Santamaría, Ignacio
dc.contributor.author Ramírez García, David
dc.contributor.author Scharf, Louis L.
dc.date.accessioned 2020-12-02T10:31:35Z
dc.date.available 2020-12-02T10:31:35Z
dc.date.issued 2019-04-18
dc.identifier.bibliographicCitation IEEE Transactions on Signal Processing, (2019), 67(11), pp.: 3028-3041.
dc.identifier.issn 1053-587X
dc.identifier.uri http://hdl.handle.net/10016/31509
dc.description This 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.
dc.description.abstract In 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.
dc.description.sponsorship The 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.
dc.format.extent 13
dc.publisher IEEE
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.
dc.subject.other Array processing
dc.subject.other Dimension
dc.subject.other Grassmann manifold
dc.subject.other Order estimation
dc.subject.other Source enumeration
dc.subject.other Subspace averaging
dc.title Subspace averaging and order determination for source enumeration
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TSP.2019.2912151
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2016-75067-C4-4-R/CARMEN
dc.relation.projectID Gobierno de España. TEC2015-69648-REDC
dc.relation.projectID Gobierno de España. BES-2017-080542
dc.relation.projectID Gobierno de España. TEC2017-92552-EXP/aMBITION
dc.relation.projectID Gobierno de España. TEC2015-69868- C2-1-R/ADVENTURE
dc.relation.projectID Gobierno de España. TEC2017-86921-C2-2-R/CAIMAN
dc.relation.projectID Comunidad de Madrid. Y2018/TCS-4705/PRACTICOCM
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 3028
dc.identifier.publicationissue 11
dc.identifier.publicationlastpage 3041
dc.identifier.publicationtitle IEEE Transactions on Signal Processing
dc.identifier.publicationvolume 67
dc.identifier.uxxi AR/0000025041
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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