$220 ARM Device Achieves 96.9% Accuracy in Radio Signal Classification
tom_doerr · x · 2026-08-08
The GitHub open-source project rtl-ml demonstrates how to run an AI model entirely offline on just $220 worth of hardware (Indiedroid Nova + RTL-SDR Blog V4) to identify real-world radio signals.
- High Accuracy & Low Power: Without cloud reliance or a GPU, it achieves 96.9% accuracy in classifying 7 types of radio signals (e.g., FM, NOAA Weather, APRS) on an ARM SBC.
- Rigorous ML Pipeline: The project trains on 800 validated real samples, uses a temporal train/test split to prevent data leakage, and tests generalization across multiple FM frequencies.
- End-to-End: Provides a complete pipeline covering signal capture, model training, and final classification.
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