Learning Sequence-Based Fingerprint for Magnetic Indoor Positioning System

Indoor magnetic-based positioning has attracted tremendous interests in recent years due to its pervasiveness and independence from extra infrastructure. Existing methods for indoor magnetic-based positioning are either point-based fingerprint matching or sequence-based fingerprint matching using the raw magnetic field strength. However, the magnetometers in smartphones are vulnerable to a few factors such as user's postures and walking speed, which causes the magnetic field strength corresponding to a location often shift in time or exhibit local distortions, thus greatly limits the positioning performance of existing methods rely on raw magnetic field strength. To this end, we observe the differences among magnetic field strength sequences are mainly attributed to small local segments, and design a new sequence-based fingerprint based on the differences among small local segments of raw MFS sequence to represent raw MFS sequence for indoor positioning. To demonstrate the utility of our proposed sequence-based fingerprint, we have performed a comprehensive experimental evaluation on two datasets, the results show that the proposed approach can significantly improve positioning performance compare with baseline methods.