  Smartphones have become a popular tool for indoor localization and position
estimation of users. Existing solutions mainly employ Wi-Fi, RFID, and magnetic
sensing techniques to track movements in crowded venues. These are highly
sensitive to magnetic clutters and depend on local ambient magnetic fields,
which frequently degrades their performance. Also, these techniques often
require pre-known mapping surveys of the area, or the presence of active
beacons, which are not always available. We embed small-volume and large-moment
magnets in pre-known locations and arrange them in specific geometric
constellations that create magnetic superstructure patterns 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
contribution is twofold. First, we deploy passive permanent magnets that do not
require a power supply, in contrast to active magnetic transmitters. Second, we
perform localization based on smartphone motion rather than on static
positioning of the magnetometer. In our previous study, we considered a single
superstructure pattern. Here, we present an extended version of that algorithm
for multi-superstructure localization, which covers a broader localization area
of the user. Experimental results demonstrate localization accuracy of 95% with
a mean localization error of less than 1m using artificial intelligence.
