Joint access point fuzzy rough set reduction and multisource information fusion for indoor Wi-Fi positioning

With the increasing maturity and popularity of wireless network techniques, indoor Wi-Fi positioning will inevitably become a significant application in indoor location-based services. In this circumstance, there is normally no control over the number of access points (APs) and the diversity of the Wi-Fi signal distribution, which may significantly deteriorate the positioning effectiveness as well as the system efficiency. To address this issue, we first adopt the fuzzy information entropy-based fuzzy rough set to conduct redundant APs reduction. Second, we calculate the Wasserstein distance between the signal distribution at the target position and the one at each Reference Point (RP) by the Wasserstein distance method. Third, the multisource information fusion method based on the Dempster-Shafer evidence theory is exerted to construct the matching RPs set. Finally, the abundant experiments and results in a realistic indoor Wi-Fi environment testify that the proposed method is able to preserve satisfactory localization performance as well as reduce the computation overhead of localization.