RSSI-based indoor localization for mobile devices has become important as the basis technology for various ubiquitous computing applications.
With the widespread use of smartphones in the last decade, device dependency must be considered to avoid performance degradation, while most of the recent localization approaches assume that all the smartphone models have the same device characteristics.
We propose a novel feature representation based on multiple RSSIs for compensating performance degradation
against smartphone models changes.
In contrast to the previous feature representation based on a single RSSI, our new feature representation, which we call Ellipsoid features, employs tuples of pair of RSSIs to eliminate device dependence in the path loss model for wave propagation.
Experimental result using smartphone devices including Android Nexus5, Nexus5X, Nexus6P, and Xperia X Performance shows that our approach achieves superior performance over the state-of-the-art indoor localization models as well as robust performance against device changes.
Robust indoor localization across smartphone models with ellipsoid features from multiple RSSIs.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 1, No. 3, pp. 103:1–103:16, 2017.