Detection and Localization of Unmanned Aerial Vehicles Based on Radar Technology

Idhis, Sally M.; Dawdi, Takwa; Nasir, Qassim; Talib, Manar Abu; Omran, Yara

doi:10.1007/978-981-16-2164-2_34

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…The challenges in countering UAVs and future trends were also covered in the survey. In [29], different approaches using radar systems for the detection and localization of UAVs in academia and industry were provided. The hardware and software methods and commonly used localization algorithms were also discussed in the survey.…”

Section: A Existing Surveys and Reviews In The Literaturementioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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Section: A Existing Surveys and Reviews In The Literaturementioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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“…For example, browser exploit packs, malware infection frameworks on the underground market, and zero-day exploits are widely available. 3 Machine learning (ML) is under artificial intelligence (AI) and has been embraced in various fields, for instance, unmanned aerial vehicle beam selections, 4,5 biomedical engineering technologies, 6 and network resource management approaches. 7 Personal information and compromised domains are purchased by attackers for launching extra attacks.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence analysis in cyber domain: A review

Zhao

Zhu

Shafik

et al. 2022

International Journal of Distributed Sensor Networks

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The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable, and timely manner. An in-depth learning algorithm is utilized for training a neural network for detecting suspicious user activities. The proposed system architecture was implemented with the help of Splunk Enterprise Edition 6.42. A data set of average feature counts has been executed through a Splunk search command in 1-min intervals. All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. The rule-based method dispensed a low quantity of irregular illustrations in contrast with the Partitioning Around Medoids method. The results in this study demonstrated that using a proportional collection of instances trained with the deep learning algorithm, a classified data set can accurately detect suspicious behavior. This method permits for the allocation of multiple log source types through a sliding time window and provides a scalable solution, which is a much-needed function.

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Doppler Radar Performance for UAV Speed Detection in mmWave/sub-THz Systems with Directional Antennas

Gaydamaka,

Samuylov,

Moltchanov

et al. 2024

Lecture Notes in Computer Science

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Detection and Localization of Unmanned Aerial Vehicles Based on Radar Technology

Cited by 3 publications

References 46 publications

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Artificial intelligence analysis in cyber domain: A review

Doppler Radar Performance for UAV Speed Detection in mmWave/sub-THz Systems with Directional Antennas

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