Detection of acoustic, electro-optical and RADAR signatures of small unmanned aerial vehicles

Hommes, Alexander; Shoykhetbrod, Alex; Noetel, Denis; Stanko, Stephan; Laurenzis, Martin; Hengy, Sébastien; Christnacher, Frank

doi:10.1117/12.2242180

Cited by 26 publications

(23 citation statements)

References 8 publications

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“…Also, they are flying at relatively much lower velocities, which decreases the Doppler signature [15,16]. Even though such examples as [11,15,17,18] exist, especially in K, S, X-band and with the exploitation of Doppler effect, generally they fail to classify other aerial objects such as birds and the background clutter due their increased sensitivity for this particular case [16]. Hence, RADAR technology has not been considered as an effective solution counter drones, especially for autonomous configurations.…”

Section: Drone Detection and Trackingmentioning

confidence: 99%

“…Hence, RADAR technology has not been considered as an effective solution counter drones, especially for autonomous configurations. On the other end, LIDAR is a relatively new technology to be used for drone surveillance task, thus only few proposals such as [11,19,20] exist in the literature. Its feasibility and cost effectiveness is still questionable due to voluminous data output and sensitivity to the clouds etc.…”

Section: Drone Detection and Trackingmentioning

confidence: 99%

“…Acoustics has been used also to detect drones by employing microphone arrays [11,21]. The aim is to classify specific sound of rotors of drones; however, they fail to achieve high accuracy and operational range.…”

Section: Drone Detection and Trackingmentioning

confidence: 99%

“…Certain systems in the market and architectures proposed by researchers offer autonomous detection, tracking and identification of the UAVs, which is a highly important operational feature. The proposed systems use either RF signal detection (used for the communication between device and the ground operator) [9], acoustics [10], RADAR [11], LIDAR [12], or common passive optics (cameras) backed by computer vision algorithms [13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning-based strategies for the detection and tracking of drones using several cameras

Unlu

Zenou

Rivière

et al. 2019

IPSJ T Comput Vis Appl

119

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Commercial Unmanned aerial vehicle (UAV) industry, which is publicly known as drone, has seen a tremendous increase in last few years, making these devices highly accessible to public. This phenomenon has immediately raised security concerns due to fact that these devices can intentionally or unintentionally cause serious hazards. In order to protect critical locations, the academia and industry have proposed several solutions in recent years. Computer vision is extensively used to detect drones autonomously compared to other proposed solutions such as RADAR, acoustics and RF signal analysis thanks to its robustness. Among these computer vision-based approaches, we see the preference of deep learning algorithms thanks to their effectiveness. In this paper, we are presenting an autonomous drone detection and tracking system which uses a static wide-angle camera and a lower-angle camera mounted on a rotating turret. In order to use memory and time efficiently, we propose a combined multi-frame deep learning detection technique, where the frame coming from the zoomed camera on the turret is overlaid on the wide-angle static camera's frame. With this approach, we are able to build an efficient pipeline where the initial detection of small sized aerial intruders on the main image plane and their detection on the zoomed image plane is performed simultaneously, minimizing the cost of resource exhaustive detection algorithm. In addition to this, we present the integral system including tracking algorithms, deep learning classification architectures and the protocols.

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Section: Drone Detection and Trackingmentioning

confidence: 99%

Section: Drone Detection and Trackingmentioning

confidence: 99%

Section: Drone Detection and Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning-based strategies for the detection and tracking of drones using several cameras

Unlu

Zenou

Rivière

et al. 2019

IPSJ T Comput Vis Appl

119

View full text Add to dashboard Cite

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“…Usually rotors of UAVs emit specific sound and therefore detection of UAVs is possible due to specific signature caused by fast rotating propellers (Hommes et al, 2016). For drone detection acoustic features are extracted and classified.…”

Section: Acoustic Detectionmentioning

confidence: 99%

Investigation of Detection Possibility of Uavs Using Low Cost Marine Radar

et al. 2019

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The technologies of Unmanned Aerial Vehicles (UAVs) are fast emerging, but as any other technology, development of UAVs provides not only benefits but also the threats. UAV technologies are developing much faster than means of their control and detection. RADAR technology is one of the means of UAV's detection. Usually, radars are expensive, and usage of high-power radiation is problematic in many cases.Today's market provides low cost marine radar working on various principles of operation. Such radar are not optimal, but could be used for UAV detection. Detection possibility of UAVs by FMCW marine radar was investigated by using two types of small UAVs as targets.

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Towards Reliable Identification and Tracking of Drones Within a Swarm

Kumari,

Lee,

Barca

et al. 2024

J Intell Robot Syst

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Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.

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Detection of acoustic, electro-optical and RADAR signatures of small unmanned aerial vehicles

Cited by 26 publications

References 8 publications

Deep learning-based strategies for the detection and tracking of drones using several cameras

Deep learning-based strategies for the detection and tracking of drones using several cameras

Investigation of Detection Possibility of Uavs Using Low Cost Marine Radar

Towards Reliable Identification and Tracking of Drones Within a Swarm

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