Journal
JOURNAL OF NAVIGATION
Volume 71, Issue 5, Pages 1055-1068Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S037346331800019X
Keywords
1. Indoor localisation; 2. Radio Map updating; 3. GPR; 4. Machine learning
Categories
Funding
- National Key RD Plan [2017YFC0804406]
- Key R&D Plan of Shandong Province [GG201709160017]
- Project of Industrial Transformation and Upgrading (Made in China 2025) [TC170A5SW]
- Open Research Fund Program of Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data [KF201802]
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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 center dot 76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy.
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