Smartphone-Based Indoor Localization With Integrated Fingerprint Signal

Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods.