Robust and Fast Similarity Search for Fingerprint Calibrations-Free Indoor Localization

Abstract

Location-based services have become of great importance to a wide range of applications in the era of mobile computing. Various wireless indoor localization techniques are emerging. Due to the wide deployment and availability of WiFi infrastructure, WiFi fingerprint-based indoor localization has become one of the most attractive localization techniques. Site survey to build WiFi fingerprint library and fingerprint matching-based real-time positioning are based on WiFi indoor localization method two basic stages. Equipment heterogeneity and environmental dynamics are two main factors affecting indoor localization accuracy. In order to solve these problems, an algorithm based on Hierarchical Edit Distance(HED) function is proposed to realize calibrations-free fingerprint comparison of heterogeneous devices. RSSI information collected by different mobile devices is transformed into AP sequence. The hierarchical energy level of each AP is calculated according to the difference of the RSSI values. Then the distance between the WiFi fingerprints is calculated by edit distance. In order to locate WiFi fingerprint RSSI information, we use HED to obtain K neighbors, and use the WKNN algorithm to predict position. In order to verify robustness and effectiveness of the algorithm, five different mobile devices were used to collect WiFi RSSI information in three different types of indoor environments. The average localization accuracy was 1.5m.