Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Besada, Juan A.; Campaña, Iván; Carramiñana, David; Bergesio, Luca; Miguel, Gonzalo de

doi:10.3390/s22010189

Cited by 13 publications

(7 citation statements)

References 35 publications

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“…The study [86] extensively covers sensor technologies for UAS detection, emphasizing MIMO radars for their enhanced detection sensitivity and real-time Doppler resolution. It demonstrates how MIMO radars, using a 32-by-8 element L-Band receiver array, effectively distinguish small drones from other objects like birds, employing machine learning for precise identification.…”

Section: Mimo Technology In Low Rcs Uav Detectionmentioning

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: Mimo Technology In Low Rcs Uav Detectionmentioning

confidence: 99%

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

Rojhani,

Shaker

2024

Remote Sensing

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“…3 shows a heatmap of the measured signal strength values versus a corresponding heatmap of the simulated values for the same locations. The simulated values were generated using (2). The similarities across both heatmaps reflect the inverse-square law in RF power levels as it applies to the distance from an emitting source.…”

Section: Sensor Modelmentioning

confidence: 99%

“…A number of incidents across the U.S. have resulted in fire fighting operations being suspended until the interfering devices no longer pose a risk to the firefighters. There exists enterprise products which seek to alleviate this problem [2], [3], but they come at expensive costs and rely on specialized hardware systems.…”

mentioning

confidence: 99%

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

2023

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The proliferation of technologically advanced and mobile devices poses risks to public safety and security. These threats can be mitigated by systems equipped to perform timely identification and tracking of devices and their operators. In pursuit of these capabilities, we propose an automated sensing platform designed specifically for tracking multiple, mobile radio frequency (RF) targets. There are a number of challenges involved with tracking multiple moving RF sources. We formulate the task as an iterative state estimation and path planning process, whereby the sensor platform first estimates the positions of the targets through observation of the RF environment and then plans and executes a movement path. By developing a sensor model informed by RF propagation theory, we construct a particle filter based state estimator with the potential to track multiple targets using only signal strength observations. In addition, we propose a path planning technique rooted in uncertainty minimization and safety based constraints. Finally, we validate the proficiency of the proposed methods with simulated experiments. Through analysis of tracking metrics and localization performance we show the benefits of our proposed active sensing techniques as they apply to tracking multiple RF targets. We demonstrate the robustness of our method to various environmental scenarios by testing with a multitude of realistic and challenging experimental parameters (e.g., speed of the sensor platform, number of targets, speed of targets, level of signal-to-noise ratio (SNR)). The results indicate that our method performs better than other state-of-theart tracking methods, with significant improvements seen in the most difficult scenarios with higher speed targets. In these and other settings, our method is more often able to localize the targets and with less error and uncertainty in position estimation. We also show that our method is computationally efficient and scales well to an increasing number of targets. INDEX TERMSMultitarget tracking, path planning, radio frequency signals, particle filters I. INTRODUCTION

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“…There are many reviews on the current state-of-the art technology in this wide variety of sensors, both commercial and academic ones (for example: [31]). Instead, in the following subsections, we are focusing on the literature with drone-based-only use cases.…”

Section: Sensing Technologiesmentioning

confidence: 99%

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

Castrillo

Manco

Pascarella

et al. 2022

Drones

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In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones.

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Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Cited by 13 publications

References 35 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

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

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