# DeepBLE: Generalizing RSSI-based Localization Across Different Devices

arXiv: https://arxiv.org/abs/2103.00252 · HTML: https://mlsanity.com/paper/2103.00252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services](https://mlsanity.com/paper/1810.04118/) (2018)

## Built on by (most cited first, 1 of 1)

- [Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization](https://mlsanity.com/paper/2211.07635/) (2022)

## Abstract (arXiv)

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.