Classification of drones based on micro-Doppler signatures with dual-band radar sensors

Zhang, Pengfei; Yang, Le; Gao, Chen; Li, Gang

doi:10.1109/piers-fall.2017.8293214

Cited by 74 publications

(45 citation statements)

References 8 publications

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“…Another study [ 47 ] showed that distinguishing between a drone and a bird can be accomplished using machine learning algorithms by extracting features from m-D signatures. Several methods suggested the use of bistatic radar, where transmitter and receiver are not collocated, or multi-static radars in order to increase accuracy of UAV detection [ 48 , 49 , 50 ]. Compared to other technologies, radar is able to provide long-range detection up to several hundred kilometers, depending on the target radar cross-section (RCS).…”

Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Lykou

Moustakas

Gritzalis

2020

Sensors

168

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As the fastest growing segment of aviation, unmanned aerial systems (UAS) continue to increase in number, technical complexity and capabilities. Numerous civilian and commercial uses are drastically transforming civil protection, asset delivery, commercial and entertaining activities. However, UAS pose significant challenges in terms of safety, security and privacy within society. An increasing phenomenon, nowadays, is drone-related incidents near airport facilities, which are expected to proliferate in frequency, complexity and severity, as drones become larger and more powerful. Critical infrastructures need to be protected from such aerial attacks, through effective counteracting technologies, risk management and resilience plans. In this paper, we present a survey of drone incidents near airports and a literature review of sensor technologies, able to prevent, detect, identify and mitigate rogue drones. We exhibit the benefits and limitations of available counter-drone technologies (C-UAS); however, defending airports against misused drone activity is a hard problem. Therefore, we analyze three realistic attack scenarios from malicious drones and propose an effective C-UAS protection plan for each case. We discuss applicability limitations of C-UAS in the aviation context and propose a resilience action plan for airport stakeholders for defending against airborne threats from misused drones.

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Section: Literature Review On Counter-drone (C-uas) Technologiesmentioning

confidence: 99%

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Lykou

Moustakas

Gritzalis

2020

Sensors

168

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“…Finally a Convolutional Neural Network (CNN), specifically AlexNet, is trained to effectively classify. In [ 71 ] data are collected from two radar sensors (in the K and X bands) and the classification task is performed both individually and for their combination. After a preliminary Principal Component Analysis (PCA)-based reduction step, a Support Vector Machine (SVM) classifier is used to discriminate among the different categories of drones.…”

Section: Literature On Drone Verification and Classificationmentioning

confidence: 99%

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Coluccia

Parisi

Fascista

2020

Sensors

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Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

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“…This shows that there is very useful information to be obtained regarding polarimetry, which can significantly aid in distinguishing between drones and birds. P. Zhang, et al [44], utilised two CW radars operating at K band and X band, to discriminate between three types of drones (helicopter, quadcopter and hexacopter). The spectrograms were decomposed using PCA and were presented across three dimensions, an SVM algorithm was then utilised to classify between the types of drones, through enclosing the clusters and defining a suitable hyperplane.…”

Section: Classification Implementationsmentioning

confidence: 99%

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Patel

Fioranelli

Anderson

2018

IET Radar, Sonar & Navigation

148

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This review explores radar based techniques currently utilised in literature to monitor small UAVs or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration is briefly outlined. Key difficulties involved in the identification and hence tracking of these 'radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and RCS characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum.

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Classification of drones based on micro-Doppler signatures with dual-band radar sensors

Cited by 74 publications

References 8 publications

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Review of radar classification and RCS characterisation techniques for small UAVs or drones

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