  Accurate smartphone localization (< 1-meter error) for indoor navigation
using only RSSI received from a set of BLE beacons remains a challenging
problem, due to the inherent noise of RSSI measurements. To overcome the large
variance in RSSI measurements, we propose a data-driven approach that uses a
deep recurrent network, DeepBLE, to localize the smartphone using RSSI measured
from multiple beacons in an environment. In particular, we focus on the ability
of our approach to generalize across many smartphone brands (e.g., Apple,
Samsung) and models (e.g., iPhone 8, S10). Towards this end, we collect a
large-scale dataset of 15 hours of smartphone data, which consists of over
50,000 BLE beacon RSSI measurements collected from 47 beacons in a single
building using 15 different popular smartphone models, along with precise 2D
location annotations. Our experiments show that there is a very high
variability of RSSI measurements across smartphone models (especially across
brand), making it very difficult to apply supervised learning using only a
subset of smartphone models. To address this challenge, we propose a novel
statistic similarity loss (SSL) which enables our model to generalize to unseen
phones using a semi-supervised learning approach. For known phones, the iPhone
XR achieves the best mean distance error of 0.84 meters. For unknown phones,
the Huawei Mate20 Pro shows the greatest improvement, cutting error by over
38\% from 2.62 meters to 1.63 meters error using our semi-supervised adaptation
method.
