Received signal strength–based localization for large space indoor environments

Abstract

WiFi-based indoor localization has attracted recent research attention. Large space layout is a special and more complex indoor environment. Most existing indoor localization methods lead to poor accuracy and many of them are not suitable for large space environments. In this article, we propose a novel approach for indoor localization and navigation. In our approach, the expensive training is avoided by utilizing the concept of pre-scheduled path and automatically mapping the WiFi fingerprints to it. For online tracing, we utilize historical sensor data to delineate users’ trajectory and calculate the similarity to all possible paths on the map, then the system chooses the most similar one as the result. The proposed work is evaluated and compared with previous methods. The results show that our approach improves accuracy by 80%.