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DC Field | Value | Language |
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dc.contributor.author | Geng, H | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Alsaadi, F | - |
dc.contributor.author | Alharbi, KH | - |
dc.contributor.author | Cheng, Y | - |
dc.date.accessioned | 2021-02-01T15:13:50Z | - |
dc.date.available | 2020-01-01 | - |
dc.date.available | 2021-02-01T15:13:50Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Signal and Information Processing over Networks, 2020 | en_US |
dc.identifier.issn | http://dx.doi.org/10.1109/TSIPN.2020.3044904 | - |
dc.identifier.issn | 2373-776X | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/22167 | - |
dc.description | © 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 |
dc.description.abstract | IEEE This paper is concerned with the multi-sensor filtering fusion problem subject to stochastic uncertainties under the Round-Robin protocol (RRP). The uncertainties originate from three sources, namely, censored observations, dynamical biases and additive white noises. To reflect the dead-zone-like censoring phenomenon, the measurement observation is described by the Tobit model where the censored region is constrained by prescribed left- and right-censoring thresholds. The bias is modeled as a dynamical stochastic process driven by a white noise in order to reflect the random behaviors of possible ambient disturbances. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. The filtering fusion is conducted via two stages: 1) the sensor observations arriving at its corresponding estimator are first leveraged to generate a local estimate, and 2) the local estimates are then gathered together at the fusion center in order to form the fused estimate. The local estimator implements a Tobit Kalman filtering algorithm on the basis of an enhanced Tobit regression model, whilst the fusion center realizes a filtering fusion algorithm in accordance with the well-known federated fusion principle. The validity of the fusion approach is finally shown via a simulation example. | en_US |
dc.description.sponsorship | Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant (RG-7-135-41); National Natural Science Foundation of China under Grants 61803074, U2030205, 61903065, 61671109, U1830207, and U1830133; China Postdoctoral Science Foundation under Grants 2017M623005, 2018M643441 and 2015M5825, and in part by the Alexander Von Humboldt Foundation of Germany | en_US |
dc.language.iso | en | en_US |
dc.subject | Sensors | en_US |
dc.subject | Uncertainty | en_US |
dc.subject | Kalman filters | en_US |
dc.subject | Protocols | en_US |
dc.subject | Stochastic processes | en_US |
dc.subject | Sensor fusion | en_US |
dc.subject | Acceleration | en_US |
dc.title | Federated Tobit Kalman Filtering Fusion with Dead-Zone-Like Censoring and Dynamical Bias under the Round-Robin Protocol | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/TSIPN.2020.3044904 | - |
dc.relation.isPartOf | IEEE Transactions on Signal and Information Processing over Networks | - |
pubs.publication-status | Published | - |
dc.identifier.eissn | 2373-776X | - |
Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
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