Comparison of Fingerprint Matching Methods for Wi-Fi Indoor Positioning

Currently, positioning in indoor environment enjoys heavy demands in many public places. Considering that methods for outdoor positioning such as satellites localization are not applicable in indoor environment, Wi-Fi indoor positioning technology, regarding signal indicators as fingerprints, is a popular approach. However, most prior researches focus on the single device in position identification. As a result, the Euclidean distance similarity method, which is widely used for fingerprint matching of a single device, is not suitable for multiple devices. To solve the problem, this research aims to find a better matching method for multiple devices. An experiment is set to find the characteristics of Wi-Fi Received Signal Strength Indicator (RSSI) and create fingerprints for every position point. Since at least 92% RSSIs at one position point centralize at a specific value in the stable environment, the most centralized value is selected as fingerprints. Using the collected data and fingerprints, two matching methods- Euclidean Distance method and Cosine Similarity method, are compared in fingerprint matching for the same and different devices. The experiment results demonstrate that, by using two methods, the precision for same device matching is similar. However, for different device matching, Cosine similarity method, which has obviously increased the match accuracy, is a better method in fingerprint matching.