Single and multiple drones detection and identification using RF based deep learning algorithm

Sazdic-Jotic, Boban; Pokrajac, Ivan; Bajčetić, Jovan; Bondžulić, Boban; Obradović, Danilo

doi:10.1016/j.eswa.2021.115928

Cited by 24 publications

(9 citation statements)

References 22 publications

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“…One of the most widely used approaches to detect the presence of a drone in no-fly zones is by sensing the RF communications between the drone and ground controllers [97]. This method leverages RF sensors working as receivers scanning for RF communication channel transmissions [97,[99][100][101][102][103][104]. The RF sensors are designed to detect the RF frequency ranges that drones use for control and data signaling with ground controllers.…”

Section: Rf Ground Communication Sensorsmentioning

confidence: 99%

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

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Section: Rf Ground Communication Sensorsmentioning

confidence: 99%

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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“…In recent years, the use of RF-based UAV detection and classification has dramatically increased. In the state of the art, many works have been completed using RF technology for UAV detection and classification [42,57,[63][64][65]. A DL approach based on RF was proposed in [63] to detect multiple UAVs.…”

Section: Uav Classification Based On ML Using Rf Analysismentioning

confidence: 99%

“…In the state of the art, many works have been completed using RF technology for UAV detection and classification [42,57,[63][64][65]. A DL approach based on RF was proposed in [63] to detect multiple UAVs. To complete the objectives of detection and classification, the authors suggested the use of a supervised DL model.…”

Section: Uav Classification Based On ML Using Rf Analysismentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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“…Kılıç et al [37] also focused on drone classification based on RF signals, achieving high accuracy rates using spectral-based audio features and a support vector machine (SVM)-based machine learning algorithm. In the same year, Sazdic-Jotic et al [38] presented a method for single and multiple drone detection and identification using RF-based deep learning algorithms, achieving high accuracy in both scenarios.…”

Section: Background and Related Workmentioning

confidence: 99%

Drone Detection and Tracking Using RF Identification Signals

Aouladhadj,

Kpre,

Deniau

et al. 2023

Sensors

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The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone’s 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

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Single and multiple drones detection and identification using RF based deep learning algorithm

Cited by 24 publications

References 22 publications

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Drone Detection and Tracking Using RF Identification Signals

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