Smart Drone Neutralization: AI Driven RF Jamming and Modulation Detection with Software Defined Radio

Abstract

The increasing use of wireless technologies in many aspects of people’s lives has led to a congested electromagnetic spectrum, making it critical to manage the limited available spectrum as efficiently as possible. This is particularly important for military activities such as electronic warfare, where jamming is used to disrupt enemy communication, self-attacking drones, and surveillance drones. However, current detection methods used by armed personnel, such as optical sensors and Radio Detection and Ranging (RADAR), do not include Radio Frequency (RF) analysis, which is crucial for identifying the signals used to operate drones. To combat security vulnerabilities posed by the rogue or unidentified transmitters, RF transmitters should be detected not only by the available data content of broadcasts but also by the physical properties of the transmitters. This requires faster fingerprinting and identifying procedures that extend beyond the traditional hand-engineered methods. In this paper, RF data from the drones’ remote controller is identified and collected using Software Defined Radio (SDR), a radio that employs software to perform signal-processing tasks that were previously accomplished by hardware. A deep learning model is then provided to train and detect modulation strategies utilized in drone communication and a suitable jamming strategy. This paper overviews Unmanned Aerial Vehicles (UAV) neutralization, communication signals, and Deep Learning (DL) applications. It introduces an intelligent system for modulation detection and drone jamming using Software Defined Radio (SDR). DL approaches in these areas, alongside advancements in UAV neutralization techniques, present promising research opportunities. The primary objective is to integrate recent research themes in UAV neutralization, communication signals, and Machine Learning (ML) and DL applications, delivering a more efficient and effective solution for identifying and neutralizing drones. The proposed intelligent system for modulation detection and jamming of drones based on SDR, along with deep learning approaches, holds great potential for future research in this field.

Keywords

UAV neutralization / Intelligent systems / Software defined radio / Deep learning / Modulation detection

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this work, the authors used ChatGPT to assist with language editing. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Acknowledgments

This work has been supported by the University of Moratuwa and the Sri Lanka Navy.

Author Contributions

S.S. coordinated all research activities and supervised the work; S.N. conceived the setup and designed the experiments; S.N. wrote the paper; all authors contributed to the data analysis and paper revision. All authors have read and agreed to the published version of the manuscript.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Available upon request.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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