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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2021

05.01.2021

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

verfasst von: Yufang Yin, Qiyu Wang, Huijie Zhang, Hong Xu

Erschienen in: Wireless Personal Communications | Ausgabe 2/2021

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Abstract

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.
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Metadaten
Titel
A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter
verfasst von
Yufang Yin
Qiyu Wang
Huijie Zhang
Hong Xu
Publikationsdatum
05.01.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07888-w

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