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dc.contributor.authorCiuonzo, D
dc.contributor.authorJavadi, S.
dc.contributor.authorMohammadi, A.
dc.contributor.authorSalvo Rossi, Pierluigi
dc.date.accessioned2021-01-21T15:26:02Z
dc.date.available2021-01-21T15:26:02Z
dc.date.created2020-11-28T23:30:09Z
dc.date.issued2020
dc.identifier.citationIEEE Transactions on Signal and Information Processing over Networks. 2020, .en_US
dc.identifier.issn2373-7778
dc.identifier.urihttps://hdl.handle.net/11250/2724174
dc.description.sponsorshipDecentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a nonlinear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleBandwidth-constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber15en_US
dc.source.journalIEEE Transactions on Signal and Information Processing over Networksen_US
dc.identifier.doi10.1109/TSIPN.2020.3037832
dc.identifier.cristin1853713
dc.description.localcode© 2020 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.en_US
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode1


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