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dc.contributor.authorMansfield, TO
dc.contributor.authorGhita, BV
dc.contributor.authorAmbroze, MA
dc.date.accessioned2017-09-01T12:44:56Z
dc.date.issued2017-04
dc.identifier.issn0003-4347
dc.identifier.issn1958-9395
dc.identifier.other3-4
dc.identifier.urihttp://hdl.handle.net/10026.1/9887
dc.description.abstract

Motivated by the geolocation requirements of future mobile network applications such as portable internet of things (IoT) devices and automated airborne drone systems, this paper aims to provide techniques for improving device geolocation estimates in urban and indoor locations. In these applications low size, weight and power are vital design constraints. This paper proposes methods for improving the geolocation estimate available to a system in indoor and urban environments without the need for addition sensing or transmitting hardware. This paper proposes novel system application techniques that enable the integration of signals of opportunity, providing a robust geolocation estimate without any additional hardware. The proposed method utilises a sinusoidal Kalman filter architecture to analyse raw radio frequency (RF) signals that surround a system in urban and indoor environments. The introduced techniques efficiently analyse the raw RF data from any signal of opportunity and combine it with higher level geolocation sensors to provide an improved geolocation estimate. The improvements achieved by the system in a range of environments have been simulated, analysed and compared to the results obtained using the prior art. These improvements have been further validated and benchmarked by hardware test. The results obtained provide evidence that the efficient use of signals of opportunity coupled with common navigation sensors can provide a robust and reliable geolocation system in indoor and urban environments.

dc.format.extent145-155
dc.languageen
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.subjectSignals of opportunity
dc.subjectKalman filtering
dc.subjectRadio navigation and geolocation
dc.titleSignals of opportunity geolocation methods for urban and indoor environments
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000398772400004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue3-4
plymouth.volume72
plymouth.publication-statusPublished
plymouth.journalAnnals of Telecommunications
dc.identifier.doi10.1007/s12243-016-0559-y
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2016-12-20
dc.rights.embargodate2018-1-4
dc.identifier.eissn1958-9395
dc.rights.embargoperiod12 months
rioxxterms.versionofrecord10.1007/s12243-016-0559-y
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved
rioxxterms.licenseref.startdate2017-04
rioxxterms.typeJournal Article/Review


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