Asymptotic Performance Analysis for Landmark Learning in Indoor Localization

Landmarks are widely used to assist localization of mobile users in indoor environments. Learning a priori unknown or imprecise landmark positions through users' localization processes can in turn improve their localization performance. In this letter, we develop an analytical framework for lan...

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Bibliographic Details
Published in:IEEE communications letters 2018-04, Vol.22 (4), p.740-743
Main Authors: Yu, Tiancheng, Shen, Yuan
Format: Article
Language:English
Subjects:
Bayes methods
Bayesian inference
Convergence
Displacement measurement
Estimation
indoor localization
landmark learning
Performance analysis
Position measurement
Q measurement
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Summary:Landmarks are widely used to assist localization of mobile users in indoor environments. Learning a priori unknown or imprecise landmark positions through users' localization processes can in turn improve their localization performance. In this letter, we develop an analytical framework for landmark learning in localization, in which the positions of the landmarks are continually refined by the inter-landmark measurements from users. We derive the limit and convergence rate of the localization accuracy in asymptotic regimes. Our results characterize the effects of the number of landmarks, the number of inter-landmark measurements, and other non-ideal effects on the localization performance. Theoretical results are validated by simulations and experiments.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2018.2791584