LAUSR

Indoor localization has become a hot topic in recent years because of its wide applications. Map matching is a popular method used to improve the localization accuracy without adding hardware. However, the existing map matching methods are usually computationally expensive, leading to the unsuitability of running on resource-limited devices such as smartphones. In this paper, we present an efficient map matching system for indoor localization, called HTrack, which uses a hidden Markov model, considering the user’s heading and spatial information. By considering user’s heading information, we significantly reduce the number of candidate states for each step, and hence improve the computational efficiency. The experimental results show that the HTrack outperforms the state-of-the-art methods (more than 25% localization accuracy improvement), and consumes about five times less energy than the state-of-the-art methods.