An Acoustic-Based Surveillance System for Amateur Drones Detection and Localization

Abstract

With the rapid rise of amateur drone usage for both recreational and malicious purposes, there is an urgent need for systems capable of detecting and localizing drones in sensitive airspaces. This paper presents an acoustic-based surveillance system for the detection and localization of amateur drones. The system relies on the unique acoustic signature of drone motors and propellers, utilizing signal processing and machine learning algorithms to identify drone presence and pinpoint their location. Experimental results demonstrate the system’s effectiveness in detecting drones under various environmental conditions and noise levels, offering a low-cost, real-time solution for drone surveillance.