An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression
Document Type
Article
Publication Date
9-1-2018
Abstract
© 2018 The Royal Institute of Navigation. Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy.
Publication Title
Journal of Navigation
Recommended Citation
Zhao, J.,
Gao, X.,
Wang, X.,
Li, C.,
Song, M.,
&
Sun, Q.
(2018).
An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression.
Journal of Navigation,
71(5), 1055-1068.
http://doi.org/10.1017/S037346331800019X
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/7758