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

Khawaja, Wahab

doi:10.36227/techrxiv.20679343.v1

Cited by 4 publications

(4 citation statements)

References 326 publications

(306 reference statements)

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“…Meanwhile, MIMO imaging radar is used to identify and follow UAVs, managing to detect them up to 150 m away even in environments with a lot of clutter. The authors of [99] discuss the use of both MIMO and beamforming technologies for the detection, tracking, and classification of aerial threats, particularly UAVs. They highlight the advantages of using MIMO radars, which include dramatically improved refresh rates, effective performance against UAV swarms, and the capability to eliminate ground clutter, thus, offering better performance compared to conventional radars.…”

Section: Fusion Of Mimo and Beamforming Technology In Low Rcs Uav Det...mentioning

confidence: 99%

Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs

Rojhani,

Shaker

2024

Remote Sensing

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Unmanned aerial vehicles (UAVs) are increasing in popularity in various sectors, simultaneously rasing the challenge of detecting those with low radar cross sections (RCS). This review paper aims to assess the current state-of-the-art in radar technology, focusing on multiple-input multiple-output (MIMO) and beamforming techniques, to address this growing concern. It explores the challenges associated with detecting UAVs in urban settings and adverse weather conditions, where traditional radar systems often do not succeed. This paper examines the existing literature and technological advancements to understand how these methodologies can significantly boost detection capabilities under the constraints of low RCS. In particular, MIMO technology, renowned for its spatial multiplexing, and beamforming, with its directional signal enhancement, are evaluated for their efficacy in the context of UAV surveillance and defense strategies. Ultimately, a comprehensive comparison is presented, drawing on a variety of studies to illustrate the combined potential of integrating these technologies, providing the way for future developments in radar system design and UAV detection.

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Section: Fusion Of Mimo and Beamforming Technology In Low Rcs Uav Det...mentioning

confidence: 99%

Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs

Rojhani,

Shaker

2024

Remote Sensing

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“…Different equipment is available on traditional aircraft for this detection. However, the detection of UAVs flying at low altitudes close to the ground, especially with their small and complex shapes, can be difficult for traditional radar systems (Hasan, Pandey, & Raj, 2022;Khawaja et al, 2022;Shao, Zhu, & Li, 2022;Song et al, 2019). This situation requires the use of alternative technologies in ATC systems (DeGarmo, 2004).…”

Section: Technology Limitations and Limited Detectabilitymentioning

confidence: 99%

Air Traffic Controllers' Perspectives on Unmanned Aerial Vehicles Integration into Non-Segregated Airspace

Tuncal

2024

Journal of Aviation

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The integration of Unmanned Aerial Vehicles (UAVs) into non-segregated airspace presents both opportunities and challenges for air traffic control (ATC). The aim of the study is to explore the perspectives of air traffic controllers on the current and anticipated challenges, workload, stress factors, performance errors, and mitigation strategies related to UAV integration. The sample consisted of 213 air traffic controllers in Türkiye. UAV operations have been available in Türkiye not only for military purposes but also for purposes such as forest fires, earthquakes, security, and others for a long time, and these UAV operations are provided with air traffic services (ATS) by air traffic controllers. The results show that air traffic controllers are concerned about mid-air collisions due to UAV technology limits and regulatory gaps, along with managing risks and unique flight characteristics. Addressing technology limitations, regulatory ambiguity, and other factors necessitates a comprehensive strategy. Solutions must prioritize collision avoidance systems, clear communication guidelines, and defined no-fly zones. It is recommended that future studies focus on the comprehensive impact of UAVs on air traffic operations and the development of regulations.

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“…From the perspective of matching swarm size, the number and routes of the drones need to be as optimal as possible to meet the task requirements. From the viewpoint of matching payload indicators, taking an electro-optical payload as an example, it is essential to jointly verify the impact of drone performance and electro-optical payload capabilities on task requirements [1][2][3][4][5][6]. Through scrutinizing UAV swarm task planning, we aim not only to formulate algorithms for superior task planning, but also to evaluate the interaction between UAV platforms and payload performances on overall mission effectiveness.…”

Section: Introductionmentioning

confidence: 99%

UAV Swarm Mission Planning and Load Sensitivity Analysis Based on Clustering and Optimization Algorithms

Yan,

Sun,

Hou

et al. 2023

Applied Sciences

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Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.

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

Cited by 4 publications

References 326 publications

Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs

Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs

Air Traffic Controllers' Perspectives on Unmanned Aerial Vehicles Integration into Non-Segregated Airspace

UAV Swarm Mission Planning and Load Sensitivity Analysis Based on Clustering and Optimization Algorithms

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