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dc.contributor.authorMehryary, S.
dc.contributor.authorMazuelas, S. 
dc.contributor.authorMalekzadehz, P.
dc.contributor.authorSpachos, P.
dc.contributor.authorPlataniotisy, K.N.
dc.contributor.authorMohammadi, A.
dc.date.accessioned2019-08-05T14:05:42Z
dc.date.available2019-08-05T14:05:42Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/20.500.11824/999
dc.description.abstractRecent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of recent interest in localization/tracking within indoor environments based on Bluetooth Low Energy (BLE) technology. The basic motive behind BLE-enabled IoT applications is to provide advanced residential and enterprise solutions in an energy efficient and reliable fashion. Although recently different state estimation (SE) methodologies, ranging from Kalman filters, Particle filters, to multiple-modal solutions, have been utilized for BLEbased indoor localization, there is a need for ever more accurate and real-time algorithms. The main challenge here is that multipath fading and drastic fluctuations in the indoor environment result in complex non-linear, non-Gaussian estimation problems. The paper focuses on an alternative solution to the existing filtering techniques and introduce/discuss incorporation of the Belief Condensation Filter (BCF) for localization via BLE-enabled beacons. The BCF is a member of the universal approximation family of densities with performance bound achieving accuracy and efficiency in sequential SE and Bayesian tracking. It is a resilient filter in harsh environments where nonlinearities and non-Gaussian noise profiles persist, as seen in such applications as Indoor Localization.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectState Estimation, Belief Condensation, Bluetooth low energy (BLE), Indoor Localization, Internet of Things.en_US
dc.titleBelief Condensation Filtering For Rssi-Based State Estimation In Indoor Localizationen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8683560en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.title2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España