Key Technologies and System Trade-offs for Detection and Localization of Amateur Drones

Azari, Mohammad Mahdi; Sallouha, Hazem; Chiumento, Alessandro; Rajendran, Sreeraj; Vinogradov, Evgenii; Pollin, Sofie

doi:10.1109/mcom.2017.1700442

Cited by 118 publications

(81 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Optimal placement of UAV for maximum coverage and reliability is studied in [4], [17] by deriving outage probability for ground and aerial relaying respectively. A tutorial and general overview on UAV applications and corresponding challenges can be found in [15], [23], [24].…”

Section: B Related Workmentioning

confidence: 99%

Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

Azari

Rosas

Pollin

2019

IEEE Trans. Wireless Commun.

Self Cite

175

131

View full text Add to dashboard Cite

The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity, and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased co-channel interference received from neighboring ground BSs is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 meters can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase their coverage and throughput from 23% to 89% and from 3.5 b/s/Hz to 5.8 b/s/Hz, respectively, outperforming ground UEs. Furthermore, our findings reveal that depending on UAV altitude, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of micro cells limit UAV performance. While UAV usage has the potential to increase area spectral efficiency (ASE) of cellular networks with moderate number of cells, they might hamper the development of future ultra dense networks.

show abstract

Section: B Related Workmentioning

confidence: 99%

Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

Azari

Rosas

Pollin

2019

IEEE Trans. Wireless Commun.

Self Cite

175

131

View full text Add to dashboard Cite

show abstract

“…While drone counter-measures have already reached a significant level of reliability, drone detection can only rely on a few effective techniques [10]. Among the various techniques, four major strategies are emerging: (i) visual detection; (ii) audio detection; (iii) radar; and, (iv) RF detection.…”

Section: Introductionmentioning

confidence: 99%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

View full text Add to dashboard Cite

We propose Picking a Needle in a Haystack (PiNcH ), a methodology to detect the presence of a drone, its current status, and its movements by leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH is fully passive and it requires just cheap and general-purpose hardware. To evaluate the effectiveness of our solution, we collected real communication traces originated by a drone running the widespread ArduCopter open-source firmware, currently mounted on-board of a wide range (30+) of commercial amateur drones. Then, we tested our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movements of the drone; and, finally, (iv) enjoy a reduced upper bound on the time required to identify a drone with the requested level of assurance. The effectiveness of PiNcH has been also evaluated in the presence of both heavy packet loss and evasion attacks. In this latter case, the adversary modifies on purpose the profile of the traffic of the drone-RC link to avoid the detection. In both the cited cases, PiNcH continues enjoying a remarkable performance. Further, the comparison against state of the art solution confirms the superior performance of PiNcH in several scenarios. Note that all the drone-controller generated data traces have been released as open-source, to allow replicability and foster follow-up. Finally, the quality and viability of our solution, do prove that network traffic analysis can be successfully adopted for drone identification and status discrimination, and pave the way for future research in the area.

show abstract

“…Drone localization has a wide range of applications in military, commercial, government, and recreational scenarios. Depending on the context, the drone may or may not participate in the localization process, either because the drone communication system might not be designed for positioning purposes, or it may be a non-cooperating and potentially malicious drone [1]- [3]. There has been some recent studies in the literature to use the radio frequency (RF) signals from drones for the purpose of detection, classification, localization, and tracking of drones, regardless of whether the drone is cooperating or not [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

Impact of 3D Antenna Radiation Patterns on TDOA-Based Wireless Localization of UAVs

Sinha

Yapıcı

Güvenç

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

View full text Add to dashboard Cite

Next big commercial applications of drones require to fly the drone beyond the visual line of sight (BVLOS). This inevitable ability to fly BVLOS will also necessitate the ability to keep track of the drone's location, in order to ensure successful completion of the intended service. In this context, we explore the fundamental limits of 3D localization of drones in conjunction with the effects of the 3D antenna radiation patterns. Although localization of drone/unmanned aerial vehicle (UAV) is a well-studied topic in the literature, its relationship to the antenna effects remains mostly unexplored. In this paper, we investigate the impact of antenna radiation pattern on the accuracy of time-difference-of-arrival (TDOA)-based localization of the UAV. Specifically, we consider a scenario where a fixed number of radio-frequency (RF) sensors, placed at some known locations on the ground, collect the TDOA measurements from the signals transmitted from the UAV, and estimate the location of the UAV from these observations. In order to study the impact of the antenna effects on the fundamental limits of the TDOA-based positioning scheme, we develop a simple analytical model to approximate the total antenna gains experienced by an air-to-ground (A2G) link, for various orientations of the antennas. We then derive the Cramer-Rao lower bound for the TDOA based localization scheme, for all three combinations of the transmit and the receive antenna orientations: vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH).

show abstract

Key Technologies and System Trade-offs for Detection and Localization of Amateur Drones

Cited by 118 publications

References 17 publications

Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

Impact of 3D Antenna Radiation Patterns on TDOA-Based Wireless Localization of UAVs

Contact Info

Product

Resources

About