LAUSR

Smartphone-based indoor localization methods are frequently employed for position estimation of users inside enclosures like malls, conferences, and crowded venues. Existing solutions extensively use wireless technologies, like Wi-Fi, RFID, and magnetic sensing. However, these approaches depend on the presence of active beacons and suitable mapping surveys of the deployed areas, which render them highly sensitive to the local ambient field clutters. Thus, current localization systems often underperform. We embed small-volume and large-moment magnets in pre-known locations and arrange them in specific geometric forms. Each constellation of magnets creates a super-structure pattern of supervised magnetic signatures. These signatures constitute an unambiguous magnetic environment with respect to the moving sensor carrier. The localization algorithm learns the unique patterns of the scattered magnets during training and detects them from the ongoing streaming of data during localization. Our work innovates regarding two essential features: first, instead of relying on active magnetic transmitters, we deploy passive permanent magnets that do not require a power supply. Second, we perform localization based on smartphone motion rather than on static positioning of the magnetometer. Therefore, we present a novel and unique dynamic indoor localization method combined with artificial intelligence (AI) techniques for post-processing. Experimental results have demonstrated localization accuracy of 95% with a resolution of less than 1m.