UAV Detector With Multi-Zone Monitoring For Securing Airports Borders And Critical Infrastructure

Introduction

This system employs artificial intelligence-powered signal analysis to detect and classify unauthorized drones with exceptional accuracy. By integrating multi-band RF spectrum monitoring (70MHz–6GHz) and deep learning algorithms , it extracts unique signal fingerprints from drone communication links, including remote control, telemetry. The core innovation is its adaptive neural network architecture , which combines convolutional neural networks (CNNs) for spectral feature extraction and recurrent neural networks (RNNs) for temporal signal pattern recognition. Real-time processing of RF signals enables identification of drone models and electronic fingerprints (e.g., modulation schemes, synchronization sequences) with 99.2% accuracy, even in noisy urban environments. Key advancements include dynamic protocol decoding​​ through cognitive radio-based parsing to detect consumer and custom-built UAVs, and multi-sensor fusion correlating RF signatures with acoustic profiles and micro-Doppler radar data to reduce false positives. The system employs reinforcement learning​​ to adaptively adjust signal-to-noise ratio (SNR) thresholds, achieving 98.5% detection probability at -120dBm sensitivity. A hybrid database stores over 10,000 verified drone fingerprints, while GAN-generated synthetic signals train the AI model to recognize emerging threats. For defense applications, it integrates with counter-UAV jammers to disrupt unauthorized drones by targeting vulnerabilities in synchronization signals.

Parameters

Interface Showcase

• As shown in the figure, the specific security effect is presented. It can accurately identify unmanned aerial vehicles (UAVs), and operate the black and white list to perfectly solve the security detection problem in the airspace.