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dc.contributor.authorOtnes, Roald Wilhelmen_GB
dc.contributor.authorZetterberg, Peren_GB
dc.contributor.authorBlouin, Stephaneen_GB
dc.contributor.authorNordenvaad, Magnus Lundbergen_GB
dc.contributor.authorAustad, Håvarden_GB
dc.contributor.authorDombestein, Elin Margrethe Bøhleren_GB
dc.date.accessioned2021-07-15T09:43:10Z
dc.date.accessioned2021-07-16T07:29:10Z
dc.date.available2021-07-15T09:43:10Z
dc.date.available2021-07-16T07:29:10Z
dc.date.issued2019
dc.identifier.citationOtnes RW, Zetterberg, Blouin S, Nordenvaad, Austad H, Dombestein EMB. Distributed fusion in underwater sensor networks: Fusing bearing information. Underwater Acoustics Conference & Exhibition (UACE). 2019:809-816en_GB
dc.identifier.urihttp://hdl.handle.net/20.500.12242/2925
dc.descriptionOtnes, Roald Wilhelm; Zetterberg, Per; Blouin, Stephane; Nordenvaad, Magnus Lundberg; Austad, Håvard; Dombestein, Elin Margrethe Bøhler. Distributed fusion in underwater sensor networks: Fusing bearing information. Underwater Acoustics Conference & Exhibition (UACE) 2019 s. 809-816en_GB
dc.description.abstractIn underwater sensor networks, distributed data fusion may be more efficient than centralized fusion because the limited data transmission capacity can make it difficult to collect all required sensor information at a centralized fusion centre. In this paper, we investigate three distributed fusion techniques applied to a network of passive acoustic underwater sensor nodes. We focus on the process of having a node combining its own bearing-to-target information with bearing-to-target information received from another node. In one of the techniques, we approximate the uncertainty in crossfixes in Cartesian coordinates by a Gaussian distribution with their second-order statistics derived from an exact distribution. The bearings and covariance matrixes are fed into a Kalman filter for tracking. The other methods are a particle filter using an exact distribution, and a distributed particle filter using an approximate likelihood representation. The performance of the methods is investigated on simulated data as well as on real-world data collected by seafloor sensor nodes during a Stockholm Archipelago sea trial in the trilateral collaborative project DUSN (Distributed Underwater Sensor Networks) between Canada, Norway, and Sweden.en_GB
dc.language.isoenen_GB
dc.relation.urihttp://www.uaconferences.org/docs/2019_papers/UACE2019_870_Otnes.pdf
dc.subjectUndervannssensoreren_GB
dc.subjectNettverken_GB
dc.subjectUndervannssensornoderen_GB
dc.subjectCanadaen_GB
dc.subjectNorgeen_GB
dc.subjectSverigeen_GB
dc.titleDistributed fusion in underwater sensor networks: Fusing bearing informationen_GB
dc.typeArticleen_GB
dc.date.updated2021-07-15T09:43:10Z
dc.identifier.cristinID1758532
dc.source.issn2408-0195
dc.type.documentJournal article
dc.relation.journalUnderwater Acoustics Conference & Exhibition (UACE)


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