An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression

作者：Zhao, Jianli^*; Gao, Xiang; Wang, Xin; Li, Chunxiu; Song, Min; Sun, Qiuxia

来源：Journal of Navigation, 2018, 71(5): 1055-1068.

DOI：10.1017/S037346331800019X

• 1. Indoor localisation
• 2. Radio Map updating
• 3. GPR
• 4. Machine learning

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.

• 出版日期2018-9
• 单位山东科技大学