Magnetic Field Strength Sequence-based Indoor Localization Using Multi-level Link-node Models

This work investigates geomagnetism-based indoor localization by exploiting the magnetometers built-in smartphones. The main challenge arises from the fact that the localization accuracy is handicapped by the limited dimensionality of the magnetometer data. To cope with this problem, the magnetic field strength (MFS) sequence has been proposed to improve the localization accuracy. However, it remains an open challenge to derive the exact location through the MFS. In this work, a novel multi-level link-node model containing geometry and topology information is first proposed to construct the MFS sequence fingerprint database which can be easily constructed by crowdsourced technology. By exploiting this database, a hybrid approach combining dynamic time warping (DTW), pedestrian dead reckoning (PDR) and k-nearest neighbor (kNN) is developed to achieve efficient MFS sequence matching and accurate indoor localization. The proposed method provides a convenient and pervasive indoor localization solution that uses only the built-in sensors of smartphones. Without knowing the initial position, the user's location can be quickly determined. Extensive experimental results confirm that the proposed approach can achieve accurate and efficient MFS sequence matching results while providing accurate initial and end position estimation in the indoor environments.