# DRaGon: Robust Emitter Geolocation via Deep Learning with NTP-Synchronized RTL-SDR Receivers
> Hamid Z.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105035994545
Jurnal / Konferensi: IEEE Asia Pacific Conference on Geoscience Electronics and Remote Sensing Technology Agers
Tahun terbit: 2025
DOI: https://doi.org/10.1109/AGERS67633.2025.11446433
ISSN: 27716619
Citations: 0

## Authors
- Hamid Z.

## Abstract
Accurate radio frequency (RF) geolocation is vital for spectrum management, public safety, and defense. The Time Difference of Arrival (TDoA) method is effective but requires sub-nanosecond synchronization, traditionally achievable only with costly GPS-disciplined hardware. In contrast, the Network Time Protocol (NTP) over public networks have millisecond-level jitter, making it not suitable for conventional TDoA. This paper introduces DRaGon (Distributed Radio Geolocation), a deep learning framework that bypasses explicit time-difference estimation by learning an end-to-end mapping from raw in-phase and quadrature (I/Q) samples to geographic coordinates. Using three low-cost RTL-SDR receivers synchronized solely by NTP over cellular networks, we trained and evaluated DRaGon on a real-world dataset of more than $\mathbf{1 9 8, 0 0 0 ~ F M}$ broadcast samples collected in Semarang, Indonesia. Results show that DRaGon achieves a mean absolute localization error of 426 meters, a 63.24% improvement over sophisticated RSSI-based trilateration baselines. These findings challenge the assumption that precise hardware synchronization is indispensable for TDoA and demonstrate that synchronization errors and multipath effects can be treated as part of a learnable $\mathbf{R F}$ fingerprint, enabling affordable and scalable RF geolocation systems.

## Keywords
- Deep learning
- Artificial intelligence
- Computer science
- Geolocation
- Computer vision
- Common emitter
- Remote sensing
- Key (lock)
- Real-time computing
- Noise (video)
- Robustness (evolution)
- Tracking (education)
- Pattern recognition (psychology)
- Artificial neural network
- Convolutional neural network
- Clutter
- Electronic engineering

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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