DOI: https://doi.org/10.36719/2663-4619/114/269-275
Asad Rustamov
Azerbaijan Technical University
https://orcid.org/0009-0002-3080-8409
asadrustamov1122@gmail.com
Tofig Bakhshiyev
Azerbaijan Technical University
Master student
https://orcid.org/0009-0002-3650-246X
baxshiyevtofiq@gmail.com
Artificial Intelligence Integrated Software-Defined Radio Antennas
for Anti-Drone Applications
Abstract
There has been a marked surge in unmanned aerial vehicle (UAV) usage, raising serious concerns about intrusions into restricted or sensitive airspace. The goal of this article is to show AI-powered SDR antennas designed to cover multiple frequency bands, enabling real-time identification and targeted interference or jamming of unauthorized UAV signals. Software-defined radio (SDR) technologies, particularly when augmented with artificial intelligence (AI), have emerged as highly adaptable and relatively low-cost platforms for detecting, classifying, and neutralizing rogue drones. Several topics have been researched including implementation challenges, limited processing capabilities on edge devices, potential vulnerabilities in radio frequency (RF) channels, and ensuring robust AI classification despite evolving signal characteristics and adversarial spoofing attempts. Collaborative or distributed SDR solutions are proposed to enhance detection accuracy by aggregating RF data from multiple vantage points, thereby overcoming individual sensor constraints. As a result of research it has been found that ongoing studies in sensor fusion, antenna design, and edge AI optimization is expected to further enhance the capabilities of these systems, providing robust and adaptable defense against unauthorized drones
Keywords: artificial intelligence, software, radio, antenna, drone, frequency