Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors

Svanström, Fredrik; Englund, Cristofer; Alonso‐Fernandez, Fernando

doi:10.1109/icpr48806.2021.9413241

Cited by 90 publications

(48 citation statements)

References 28 publications

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“…Both audio and video streams are then processed to extract features which are then fed to a classifier to perform the detection. The results show the audio-assisted system (95.74%) significantly improves the performance of the visual detection [130] develop a drone detection and tracking system with sensor fusion extension to perform sophisticated detection decision based on outputs of multiple sensors (acoustic sensors, thermal cameras, and daylight cameras) with the possibility to configure sensors selection and weighted outputs of the selected sensors. However, with widely available settings of the sensors selection and fusion, the accuracy performance are not evaluated for hybrid detection.…”

Section: Sensor Fusionmentioning

confidence: 98%

“…However, with widely available settings of the sensors selection and fusion, the accuracy performance are not evaluated for hybrid detection. Similar to [130], [135], authors in [136] and [137] propose fusion of visual and radar detection. A Scanning Surveillance Radar System (SSRS) aided with camera is used to detect a drone using radar whereas it its identification is further confirmed by the camera.…”

Section: Sensor Fusionmentioning

confidence: 99%

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On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, costeffective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.

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Section: Sensor Fusionmentioning

confidence: 98%

Section: Sensor Fusionmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

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“…As noted in [32], there is little publicly available information on this topic. Recently, a public benchmark was proposed in [37] for the assessment of the efficiency of detection means. Reference [38] provides some figures for tested mitigation means.…”

Section: Data About the Efficiency Of Technical Meansmentioning

confidence: 99%

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

et al. 2022

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Drone expansion needs to be considered as a menace in cases of negligent, illicit, or non-cooperative use. In the case of airports, a complete protection against drone intrusion should rely on an intrusion management system, aiming at avoiding the closure of the airport. This system requires the setting of proper risk assessment methodologies for airport operations, to explicitly consider the features of drone intrusion, possibly from a quantitative point of view. This work proposes a methodological framework for the risk assessment of drone intrusions in airports, tailored on drone-intrusion features, airport features, and current operations, and considering both safety-related and security-related causes. The framework is based on the combination of model-based and data-driven approaches in order to: (i) estimate an airport vulnerability index, to measure the susceptibility of the airport to drone intrusions, based on reference datasets; (ii) specify a set of event trees to evaluate the risks of the different threat scenarios related to drone intrusions. The proposed methodological framework is applied to a concrete case study, related to Milan Malpensa airport. The achieved results show the effectiveness of the approach and elicit further requirements for counter-drone systems in airports based on the assessed risks.

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“…The applied sensors for drone surveillance: camera (mono, RGB [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ], multi-, hyperspectral, short-/longwave infrared [ 14 ]), radar [ 15 ], radio direction finder [ 16 , 17 , 18 ], acoustic (single [ 14 , 19 , 20 ], array, matrix) and laser detection and ranging. Various fusion techniques [ 14 , 21 , 22 ] have also appeared, mainly with visual, infrared, and acoustic sensors. In the case of ground transit, the use of geophones is also common [ 23 , 24 ].…”

Section: Related Literaturementioning

confidence: 99%

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

Kovács

Bolemányi

Botzheim

2022

Sensors

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This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1×1×1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

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Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors

Cited by 90 publications

References 28 publications

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

A Methodological Framework for the Risk Assessment of Drone Intrusions in Airports

Placement of Optical Sensors in 3D Terrain Using a Bacterial Evolutionary Algorithm

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