✨ This project focuses on RF-based data acquisition and machine learning classification for drone detection. RC drone receivers emit PWM (Pulse Width Modulation) signals across multiple channels; the system captures these raw RF signals, extracts signal-to-noise ratio (SNR) features, and trains a machine learning model to identify drone presence and activity patterns.
The data pipeline reads 6-channel PWM data via a low-level C acquisition module, processes the raw signal through a Python utility layer for noise filtering and feature extraction, and feeds structured datasets into Jupyter-based ML experiments (run on Google Colab). Visualisations and evaluation metrics confirm model accuracy across different flight scenarios.
