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RF Capture Validation

This project uses real-world RF data captured with a software-defined radio (SDR).
The RF capture chain was validated for stability prior to any dataset construction or ML modeling.

Hardware & Setup

  • SDR: Nooelec NESDR SMArt v5 (RTL2832U + R820T)
  • Host OS: macOS
  • Sample Rate: 1.024 MS/s
  • Gain Control: Manual (no AGC)
  • IQ Format: Unsigned 8-bit interleaved I/Q

Validation Methodology

RF stability was verified by:

  • Capturing long IQ recordings
  • Computing power spectral density (PSD) from early and late time slices
  • Comparing spectra to detect frequency drift or instability

A persistent PLL not locked warning was observed from the tuner, but its impact was evaluated empirically via spectral stability checks.

Results

  • FM Broadcast (100 MHz)
    Wideband FM energy remained time-stable across capture windows.

    FM PSD Stability

  • NOAA Weather Radio (162.5 MHz)
    Narrowband carrier remained sharply defined and fixed in frequency across time slices.

    NOAA PSD Stability

Conclusion

  • PSD peaks align across time slices for both wideband and narrowband signals
  • No observable frequency drift or spectral smearing
  • PLL warnings confirmed non-impacting for spectral-feature-based ML tasks

The RF capture pipeline is therefore considered stable and suitable for downstream spectrogram generation and machine learning.

About

End-to-end RF machine learning pipeline for analyzing raw SDR IQ data using deterministic DSP preprocessing and reproducible ML workflows. The system is config-driven, experiment-logged, and cloud-portable, with strict separation of code, data, and artifacts. Designed to mirror industry RF/ML systems rather than notebook-centric demos.

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