Online Radio Map Update Based on a Marginalized Particle Gaussian Process

In this paper, a novel scheme is reported to adapt radio maps to environmental dynamics in an online fashion by combining crowdsourcing and gaussian process regression (GPR). Specifically, a Marginalized Particle Gaussian Process (MPGP) is adopted to recursively fuse crowdsourced fingerprints with a...

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Bibliographic Details
Main Authors: Xu, Zhendong, Huang, Baoqi, Jia, Bing, Li, Wuyungerile
Format: Conference Proceeding
Language:English
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Summary:In this paper, a novel scheme is reported to adapt radio maps to environmental dynamics in an online fashion by combining crowdsourcing and gaussian process regression (GPR). Specifically, a Marginalized Particle Gaussian Process (MPGP) is adopted to recursively fuse crowdsourced fingerprints with an existing offline radio map. The advantages of the proposed scheme lie in the efficiency and scalability in comparison with the traditional approaches. Extensive experiments are carried out in a real scenario of nearly 1000 m 2 during five months, and a comparison is made with several existing popular solutions. It is shown that the proposed scheme outperforms its counterparts in terms of both robustness and accuracy.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683485