Towards RF-based Localization of a Drone and Its Controller

Nguyen, Phuc; Kim, Sang Woo; Miao, Jian; Hesselius, Daniel; Kenneally, Erin; Massey, Daniel; Frew, Eric W.; Han, Richard; Vu, Tam

doi:10.1145/3325421.3329766

Cited by 37 publications

(34 citation statements)

References 16 publications

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“…These technologies cannot be used to localize spy radars as we cannot train the spy radar's information in the fingerprinting. Nguyen et.al [27] proposed Trilateration Localization to localize drone at different anchors, which brings insight for us to localize a spy radar.…”

Section: Related Workmentioning

confidence: 99%

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

Qiu¹,

Zhang²,

Wei³

et al. 2022

Preprint

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Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for its unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also lead to serious security and privacy issues. For example, a user's daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and localize such spy radars. In this paper, we propose 𝑅𝑎𝑑𝑎𝑟 2 , a practical passive spy radar detection and localization system using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novel Frequency Component Detection method to detect the existence of mmWave signal, distinguish between mmWave radar and WiGig signals using a convolutional neural network (CNN) based waveform classifier, and localize spy radars using the trilateration method based on the detector's observations at multiple anchor points. Not only does 𝑅𝑎𝑑𝑎𝑟 2 work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we perform extensive experiments to evaluate the effectiveness and robustness of 𝑅𝑎𝑑𝑎𝑟 2

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Section: Related Workmentioning

confidence: 99%

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

Qiu¹,

Zhang²,

Wei³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, as discussed in their paper, weight of radar unit with the integrated dome array is about 1.78kg, therefore, the operation time of UAV will be influenced greatly. Different from UAV positioning, Nguyen et al [84] designed an RF-based localisation system focused on the detection and tracking of UAV and its controller with Fig. 6 AOA based localisation mechanism AOA measurements.…”

Section: Aoa Based Localisation Mechanismmentioning

confidence: 99%

A Survey on Radio Frequency based Precise Localisation Technology for UAV in GPS-denied Environment

Yang

2021

J Intell Robot Syst

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Owing to the specific characteristics of Unmanned Aerial Vehicles (UAVs), the demands and applications increase dramatically for them being deployed in confined or closed space for surveying, inspection or detection to substitute human. However, Global Positioning System (GPS) may lose effectiveness or become unavailable due to the potential signal block or interference in that operational environment. Under such circumstances, an imperative requirement on new positioning technology for UAV has emerged. With the rapid development of Radio Frequency (RF) based localisation technologies, leveraging small wireless sensor nodes for low-cost, low latency, low energy consumption and accurate localisation on UAV has received significant attention. However, no up-to-date review has been conducted in this area so far. Therefore, this paper aims to give a comprehensive survey on the RF based localisation systems with different radio communication technologies and localisation mechanisms on UAV positioning. Toward this end, an exhaustive evaluation framework is first established to evaluate the performance of each system on UAV positioning from different perspectives. Particularly, the Ultra-wideband (UWB) based system with time-based mechanisms is highlighted for UAV positioning under the consideration of the proposed evaluation framework. Finally, an intensive analysis is conducted about the current challenges and the potential research issues in this area in order to identify the promising directions for future research.

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“…A unidirectional antenna would provide triangulation capabilities for the drones with an associated software pattern to contr ol the movements of the drone. A single antenna and the rotational maneuvering capabilities would allow the drone swarm to provide an approximate triangulation of rogue RF signals (Nguyen et al, 2019). A single rotating antenna can detect a spoofed signal using a spatial processing method of anisotropy to decipher authentic or spoofed signals (Wang, Li, & Lu, 2017).…”

Section: Hardware Requirementsmentioning

confidence: 99%

“…The sharing of telemetry data among the drones in a f leet could also deliver a stream of information for proactive measures such as area avoidance or active monitoring. If one drone enters into a troubled state (Palmer & Geis, 2017), the other drones in the fleet could avoid the geographic area or enter an active monitoring mode to provide triangulation (Nguyen et al, 2019), video recording, or additional telemetry data recording for later forensic analysis.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Sensing Radio Frequency Spectrum Attacks on Medical Delivery Drones

Kulp

Mei

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Drone susceptibility to jamming or spoofing attacks of GPS, RF, Wi-Fi, and operator signals presents a danger to future medical delivery systems. A detection framework capable of sensing attacks on drones could provide the capability for active responses. The identification of interference attacks has applicability in medical delivery, disaster zone relief, and FAA enforcement against illegal jamming activities. A gap exists in the literature for solo or swarm-based drones to identify radio frequency spectrum attacks. Any nondelivery specific function, such as attack sensing, added to a drone involves a weight increase and additional complexity; therefore, the value must exceed the disadvantages. Medical delivery, high-value cargo, and disaster zone applications could present a value proposition which overcomes the additional costs. The paper examines types of attacks against drones and describes a framework for designing an attack detection system with active response capabilities for improving the reliability of delivery and other medical applications.

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Towards RF-based Localization of a Drone and Its Controller

Cited by 37 publications

References 16 publications

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

A Survey on Radio Frequency based Precise Localisation Technology for UAV in GPS-denied Environment

A Framework for Sensing Radio Frequency Spectrum Attacks on Medical Delivery Drones

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