Abstract
Recently, the indoor positioning system (IPS) based on received signal strength (RSS) has received great attention in both industry and academic fields due to the ubiquity of wireless local area networks (WLAN). In general, Wi-Fi signal is suffering from two major limitations: first, the signal suffers from variation overtime. Secondly, the signal distribution recording on some devices can be more complex (multivariate distribution). In this paper, in order to take into account the full distribution of the RSS, we propose multivariate Kullback-Leibler divergence and Jensen-Shannon divergence (JSD) derived from Kullback-Leibler divergence (KLD) to measure the probability distribution among the fingerprint database. The proposed algorithm results showed high accuracy with localization error accuracy less than 1 m.
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