DeepRF: Deep Learning for RF-based Localization and Mapping with Synthetic Data

DeepRF addresses RF-based localization and mapping using synthetic datasets and deep learning (DL). Creating large real-world wireless signal datasets is difficult, but ray-tracing and simulation advancements enable efficient synthetic dataset generation. The project aims to improve non-lineof-sight (NLOS) wireless device localization and automate digital map reconstruction using DL to interpret RF signals. Synthetic datasets from advanced ray-tracing allow comprehensive investigation of complex RF-based problems, which are challenging to study in real-world environments. DeepRF aims to create efficient DL models for localization and mapping, potentially aiding smart city applications using ambient RF signals. The project will contribute insights into input representations, data quality considerations, and strategies for addressing multiple virtually feasible solutions.