Publication: Subspace averaging and order determination for source enumeration
dc.affiliation.dpto | UC3M. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA) | es |
dc.contributor.author | Garg, Vaibhav | |
dc.contributor.author | Santamaría, Ignacio | |
dc.contributor.author | Ramírez García, David | |
dc.contributor.author | Scharf, Louis L. | |
dc.contributor.funder | Comunidad de Madrid | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es |
dc.date.accessioned | 2020-12-02T10:31:35Z | |
dc.date.available | 2020-12-02T10:31:35Z | |
dc.date.issued | 2019-04-18 | |
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. | en |
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. | en |
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. | es |
dc.description.status | Publicado | |
dc.format.extent | 13 | |
dc.identifier.bibliographicCitation | IEEE Transactions on Signal Processing, (2019), 67(11), pp.: 3028-3041. | en |
dc.identifier.doi | https://doi.org/10.1109/TSP.2019.2912151 | |
dc.identifier.issn | 1053-587X | |
dc.identifier.publicationfirstpage | 3028 | |
dc.identifier.publicationissue | 11 | |
dc.identifier.publicationlastpage | 3041 | |
dc.identifier.publicationtitle | IEEE Transactions on Signal Processing | en |
dc.identifier.publicationvolume | 67 | |
dc.identifier.uri | https://hdl.handle.net/10016/31509 | |
dc.identifier.uxxi | AR/0000025041 | |
dc.publisher | IEEE | en |
dc.relation.projectID | Gobierno de España. TEC2016-75067-C4-4-R/CARMEN | es |
dc.relation.projectID | Gobierno de España. TEC2015-69648-REDC | es |
dc.relation.projectID | Gobierno de España. BES-2017-080542 | es |
dc.relation.projectID | Gobierno de España. TEC2017-92552-EXP/aMBITION | es |
dc.relation.projectID | Gobierno de España. TEC2015-69868- C2-1-R/ADVENTURE | es |
dc.relation.projectID | Gobierno de España. TEC2017-86921-C2-2-R/CAIMAN | es |
dc.relation.projectID | Comunidad de Madrid. Y2018/TCS-4705/PRACTICOCM | es |
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.accessRights | open access | en |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Array processing | en |
dc.subject.other | Dimension | en |
dc.subject.other | Grassmann manifold | en |
dc.subject.other | Order estimation | en |
dc.subject.other | Source enumeration | en |
dc.subject.other | Subspace averaging | en |
dc.title | Subspace averaging and order determination for source enumeration | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- subspace_IEE-TSP_2019.pdf
- Size:
- 534.66 KB
- Format:
- Adobe Portable Document Format