Scale-invariant subspace detectors based on first- and second-order statistical models

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dc.contributor.author Santamaría, Ignacio
dc.contributor.author Scharf, Louis L.
dc.contributor.author Ramírez García, David
dc.date.accessioned 2020-12-09T10:50:23Z
dc.date.available 2020-12-09T10:50:23Z
dc.date.issued 2020-11-10
dc.identifier.bibliographicCitation IEEE Transactions on Signal Processing, 2020, v.68, pp.: 6432-6443.
dc.identifier.issn 1053-587X
dc.identifier.uri http://hdl.handle.net/10016/31549
dc.description.abstract The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.
dc.description.sponsorship The work by I. Santamaria was supported by the Ministerio de Ciencia e Innovación of Spain, and AEI/FEDER funds of the E.U., under grants TEC2016-75067-C4-4-R (CARMEN) and PID2019-104958RB-C43 (ADELE). The work by L. Scharf is supported by the Air Force Office of Scientific Research under contract FA9550-18-1-0087, and by the National Science Foundation (NSF) under contract CCF-1712788. The work of D. Ramírez was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grant TEC2017-86921-C2-2-R (CAIMAN), and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM).
dc.format.extent 11
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
dc.subject.other Detection
dc.subject.other Generalized Likelihood Ratio (GLR)
dc.subject.other Likelihood
dc.subject.other Multi-sensor array
dc.subject.other Multivariate Normal Model (MVN)
dc.subject.other Scale-invariant detector
dc.subject.other Subspace signals
dc.title Scale-invariant subspace detectors based on first- and second-order statistical models
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TSP.2020.3036725
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2016-75067-C4-4-R/CARMEN
dc.relation.projectID Gobierno de España. PID2019-104958RB-C43/ADELE
dc.relation.projectID Gobierno de España. TEC2017-92552-EXP/aMBITION
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 6432
dc.identifier.publicationlastpage 6443
dc.identifier.publicationtitle IEEE TRANSACTIONS ON SIGNAL PROCESSING
dc.identifier.publicationvolume 68
dc.identifier.uxxi AR/0000026152
dc.contributor.funder Comunidad de Madrid
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