Confidence-Interval-Fuzzy-Model-Based Indoor Localization

In this paper, an efficient indoor localization algorithm based on the confidence-interval fuzzy model is presented. The width of the confidence interval is essential within the proposed fingerprinting method for calculating weights, which are then taken into account while searching for the K nearest neighbors in the database of fingerprints. For each beacon in the test room, a new confidence-interval fuzzy path-loss model composed of several local linear models is constructed. The map of fingerprints is then constructed of a set of confidence-interval fuzzy models. By their consideration, the localization accuracy is significantly improved in comparison with other commonly used path-loss models. The most important novelty of this paper is the introduction of the confidence interval within the fingerprinting method, which additionally improves localization results. The platform of the localization system is developed on the basis of a smartphone and Bluetooth beacons. Therefore, the localization algorithm has to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone.