Radar Systems and Challenges for C-UAV

Wellig, Peter; Speirs, Peter J.; Schuepbach, Christof; Oechslin, R.; Renker, Matthias; Boeniger, Urs; Pratisto, Hans

doi:10.23919/irs.2018.8448071

Cited by 47 publications

(23 citation statements)

References 9 publications

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“…Radar was regarded as a limited solution for drone detection due to inflexible radar cross sections (RCS) [7], but recent radar technology advances enable arbitrary drone detection with acceptable identification rate [8]. Thus, radar is becoming adopted for long range drone detection [9], but its use also suffers from national regulations, such as RF license policies [10].…”

Section: Introductionmentioning

confidence: 99%

Survey on Anti-Drone Systems: Components, Designs, and Challenges

et al. 2021

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This paper presents a comprehensive survey on anti-drone systems. After drones were released for non-military usages, drone incidents in the unarmed population are gradually increasing. However, it is unaffordable to construct a military grade anti-drone system for every private or public facility due to installation and operation costs, and regulatory restrictions. We focus on analyzing antidrone system that does not use military weapons, investigating a wide range of anti-drone technologies, and deriving proper system models for reliable drone defense. We categorized anti-drone technologies into detection, identification, and neutralization, and reviewed numerous studies on each. Then, we propose a hypothetical anti-drone system that presents the guidelines for adaptable and effective drone defense operations. Further, we discuss drone-side safety and security schemes that could nullify current anti-drone methods, and propose future solutions to resolve these challenges.

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Section: Introductionmentioning

confidence: 99%

Survey on Anti-Drone Systems: Components, Designs, and Challenges

et al. 2021

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“…In this paper, labelled feature vectors from radar observation data are selected to construct a training database, and the supervised learning strategy is taken. The unknown target track is converted in to the feature vector (8) and applied on the trained classifier to determine the target type. Therefore, it is necessary to select a proper learning machine.…”

Section: A Random Forest Classification Modelmentioning

confidence: 99%

“…In many representative surveillance environments, there exists another type of low altitude noncooperative targets: birds. Existing works demonstrate high radar echo similarities for drones and birds [7,8,9]. The high similarity brings great difficulties in distinguishing drones and birds from radar viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data

Liu¹,

Chen

2021

IEEE Access

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“…Unmanned Aerial Vehicles (UAVs) have applications in several areas nowadays [1], one of which is the defense industry. The small size and ability to ly at low altitudes make the UAVs practically invisible to conventional radar systems [2] at long ranges. Moreover, stealth technology is the most essential part of the current and future combat aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

Doors in the sky: Detection, localization and classification of aerial vehicles using laser mesh

Khawaja¹,

Ozturk²,

Güvenç³

2021

ITU-J FET

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Stealth technology and Unmanned Aerial Vehicles (UAVs) are expected to dominate current and future aerial warfare. The radar systems at their maximum operating ranges, however, are not always able to detect stealth and small UAVs mainly due to their small radar cross sections and/or low altitudes. In this paper, a novel technique as an alternative to radar technology is proposed. The proposed approach is based on creating a mesh structure of laser beams initiated from aerial platforms towards the ground. The laser mesh acts as a virtual net in the sky. Any aerial vehicle disrupting the path of the laser beams are detected and subsequently localized and tracked. As an additional feature, steering of the beams can be used for increased coverage and improved localization and classification performance. A database of different types of aerial vehicles is created artificially based on Gaussian distributions. The database is used to develop several Machine Learning (ML) models using different algorithms to classify a target. Overall, we demonstrated through simulations that our proposed model achieves simultaneous detection, classification, localization, and tracking of a target.

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Radar Systems and Challenges for C-UAV

Cited by 47 publications

References 9 publications

Survey on Anti-Drone Systems: Components, Designs, and Challenges

Survey on Anti-Drone Systems: Components, Designs, and Challenges

Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data

Doors in the sky: Detection, localization and classification of aerial vehicles using laser mesh

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