Drone Detection and Identification Using SDR: Analysis of DJI Mini 2 Drone ID Signals

Abstract

The increasing adoption of Unmanned Aerial Vehicles (UAVs) for both commercial and recreational purposes has raised significant security and privacy concerns. DJI OcuSync 2.0, a proprietary communication protocol used in DJI drones, enables high-definition video transmission and telemetry over dual-frequency bands (2.4 GHz and 5.8 GHz). Detecting and identifying OcuSync signals in a crowded RF environment is crucial for effective drone monitoring and threat mitigation. This study presents an SDR-based detection system utilizing the USRP B210 with a 50 MHz sampling rate to capture OcuSync signals. Signal analysis is performed using Short-Time Fourier Transform (STFT) and Welch’s method for estimating Power Spectral Density (PSD). A Non-Parametric Amplitude Quantization Method (NPAQM) is implemented for dynamic threshold estimation to improve detection sensitivity. The system is tested under varying Signal-to-Noise Ratio (SNR) conditions, demonstrating high detection accuracy and robustness against interference. The proposed system provides a reliable framework for real-time OcuSync signal identification and can be adapted for broader UAV detection applications.