Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques

The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intelligence-...

Full description

Saved in:
Bibliographic Details
Published in:Telecom (Basel) 2024-12, Vol.5 (4), p.1286-1308
Main Authors: López-Muñoz, Pablo, San Frutos, Luis Gimeno, Abarca, Christian, Alegre, Francisco José, Calle, Jose Luis, Monserrat, Jose F.
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Algorithms
Artificial intelligence
autoencoders
Cameras
Classification
Computer vision
Control algorithms
Design
Drone aircraft
Drones
Efficiency
Espionage
Intelligence
Localization
Modules
Neural networks
Object recognition
Radio signals
Radio spectra
SDR
Sensors
Sequences
Software radio
Support vector machines
Surveillance
Tracking
UAV
Unmanned aerial vehicles
YOLOv10
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intelligence-based methods to improve system accuracy. The first method uses a software-defined radio (SDR) to analyze the radio spectrum, employing autoencoders to detect drone control signals and identify the presence of these devices. The second method is a computer vision module consisting of fixed cameras and a PTZ camera, which uses the YOLOv10 object detection algorithm to identify UAVs in real time from video sequences. Additionally, this module integrates a localization and tracking algorithm, allowing the tracking of the intruding UAV’s position. Experimental results demonstrate high detection accuracy, a significant reduction in false positives for both methods, and remarkable effectiveness in UAV localization and tracking with the PTZ camera. These findings position the proposed system as a promising solution for security applications.
ISSN:2673-4001
2673-4001
DOI:10.3390/telecom5040064