(A) multi-classifier approach for WLAN fingerprint-based positioning

Abstract

WLAN fingerprint-based positioning system is a viable solution for estimating the location of the mobile station. Many researchers have applied various machine learning techniques to the WLAN fingerprint-based positioning system to make a more accurate system. However, due to the noisy characteristics of the RF signal and lack of the study on environmental factors affecting propagation of the signals, the accuracy of previously suggested systems was highly dependent on environmental conditions. In this paper, we develop multi-classifier for WLAN fingerprint-based positioning system with a combining rule. According to the experiments of the multi-classifier performed in various environments, combining a multiple number of classifiers turned out to mitigate the environment-dependent characteristic of the classifiers. The performance of multi-classifier outperformed other single classifiers in all test environments; the average error distance and standard deviation of the error distance were improved by multi-classifier in all test environments.