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Maximum likelihood localization of a network of moving agents from ranges, bearings and velocity measurements

2024-08Artigo científico Acesso aberto
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dc.contributor.authorValdeira, Filipa
dc.contributor.authorSoares, Cláudia
dc.contributor.authorGomes, João
dc.contributor.institutionNOVALincs
dc.contributor.institutionDI - Departamento de Informática
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblEuropean Association for Signal Processing | Elsevier
dc.date.accessioned2025-02-06T21:18:35Z
dc.date.available2025-02-06T21:18:35Z
dc.date.issued2024-08
dc.descriptionFunding Information: This work was supported by Recovery and Resilience Plan and NextGeneration EU Funds through Project Artificial Intelligence Fights Space Debris [ C626449889-0046305 ]. Publisher Copyright: © 2024 The Author(s)
dc.description.abstractLocalization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer great performance, they are unavailable in scenarios like indoor or underwater environments, and, for large networks, the instrumentation cost is prohibitive. We develop a localization algorithm from ranges and bearings, suitable for generic mobile networks. Our algorithm is built on a tight convex relaxation of the Maximum Likelihood position estimator. To serve positioning to mobile agents, a horizon-based version is developed accounting for velocity measurements at each agent. To solve the convex problem, a distributed gradient-based method is provided. This constitutes an advantage over centralized approaches, which usually exhibit high latency for large networks and present a single point of failure. Additionally, the algorithm estimates all required parameters and effectively becomes parameter-free. Our solution to the dynamic network localization problem is theoretically well-founded and still easy to understand. We obtain a parameter-free, outlier-robust and trajectory-agnostic algorithm, with nearly constant positioning error regardless of the trajectories of agents and anchors, achieving better or comparable performance to state-of-the-art methods, as our simulations show. Furthermore, the method is distributed, convex and does not require any particular anchor configuration.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent987351
dc.identifier.doi10.1016/j.sigpro.2024.109471
dc.identifier.issn0165-1684
dc.identifier.otherPURE: 106524819
dc.identifier.otherPURE UUID: 8007dde9-f7ac-4419-92dc-eb9ac55cbc69
dc.identifier.otherScopus: 85189672936
dc.identifier.otherORCID: /0000-0003-3071-6627/work/177535916
dc.identifier.urihttp://hdl.handle.net/10362/178552
dc.identifier.urlhttps://www.scopus.com/pages/publications/85189672936
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50009%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F00297%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT
dc.relationCenter for Mathematics and Applications
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.subjectConvex optimization
dc.subjectDistributed optimization
dc.subjectDynamic network localization
dc.subjectHybrid measurements
dc.subjectMaximum likelihood estimation
dc.subjectControl and Systems Engineering
dc.subjectSoftware
dc.subjectSignal Processing
dc.subjectComputer Vision and Pattern Recognition
dc.subjectElectrical and Electronic Engineering
dc.titleMaximum likelihood localization of a network of moving agents from ranges, bearings and velocity measurementsen
dc.typejournal article
degois.publication.titleSignal Processing
degois.publication.volume221
dspace.entity.typePublication
oaire.awardNumberUIDB/50009/2020
oaire.awardNumberUIDB/00297/2020
oaire.awardNumberUIDP/00297/2020
oaire.awardNumberUIDB/04516/2020
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50009%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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project.funder.nameFundação para a Ciência e a Tecnologia
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project.funder.nameFundação para a Ciência e a Tecnologia
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