Micro-Doppler based detection and tracking of UAVs with multistatic radar

Hoffmann, Folker; Ritchie, Matthew; Fioranelli, Francesco; Charlish, Alexander; Griffiths, Hugh

doi:10.1109/radar.2016.7485236

Cited by 149 publications

(77 citation statements)

References 10 publications

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“…Audio detection resorts to the generation of an audio signature of the drone propellers to be used to train a classifier [12]; such a technique further requires arrays of microphones to be deployed in the area to be monitored [13]. Conversely, radar detection involves the transmission of RF signals to receive an RF echo that can be identified and tracked [14]. Radars, while being the most powerful among the detection strategies, are expensive equipment that eventually might not be effective to detect small devices such as drones-since the radar signature is quite blurred.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…A G2A channel is observed to be significantly different than A2A and ground-to-ground (G2G) communication due to the high impact of altitude on the channel parameters including path loss exponent, small-scale fading and shadowing effect. To clarify this fact, we consider two extreme 4 cases for a given ground terminal that aims to communicate with a drone seen by an elevation angle of θ:…”

Section: Why Fly Higher: a Better Los Experiencementioning

confidence: 99%

“…Frequency sweeping is used to scan for the presence of drones and classification techniques are employed to determine the nature of the detected drone and to track it. Such solutions are very powerful but require the purchase of large devices with fixed coverage radius and the small size of the ADrs poses great detection limitations [4]. Solutions based on video detection require the presence of either distributed camera equipment or 360 • video recording devices [2].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Abstract-The use of amateur drones (ADrs) is expected to significantly increase over the upcoming years. However, regulations do not allow such drones to fly over all areas, in addition to typical altitude limitations. As a result, there is an urgent need for ADrs surveillance solutions. These solutions should include means of accurate detection, classification, and localization of the unwanted drones in a no-fly zone. In this paper, we give an overview of promising techniques for modulation classification and signal strength based localization of ADrs by using surveillance drones (SDrs). By introducing a generic altitude dependent propagation model, we show how detection and localization performance depend on the altitude of SDrs. Particularly, our simulation results show a 25 dB reduction in the minimum detectable power or 10 times coverage enhancement of an SDr by flying at the optimum altitude. Moreover, for a target no-fly zone, the location estimation error of an ADr can be remarkably reduced by optimizing the positions of the SDrs. Finally, we conclude the paper with a general discussion about the future work and possible challenges of the aerial surveillance systems.

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“…The state of moving targets at time k is a twodimensional vector; position and speed ′ components [8,16];…”

Section: The Tracking Modelmentioning

confidence: 99%

“…The detection, tracking and then the classification of moving targets are of interest in literature micro-Doppler signatures had been used in [7,8] to discriminate and to track the small UAV using the multi-static radar system. [3] presents the investigation of the human motion action under four different environments.…”

Section: Introductionmentioning

confidence: 99%

Moving Target Tracking and Classification Based on Micro-Doppler Signatures

Saeed¹,

Al-Muifraje²,

Sadah³

et al. 2019

IJIES

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This paper presents the results of accompanying classification of tracking for different classes of targets such as a car (moving non-rigid target), moving people (slow moving non-rigid target). The Data are collected using frequency modulation continuous wave (FMCW)radar, while different neural algorithms are considered for classification of targets recorded. A Doppler and Micro Doppler have been used as target features, while short time Fourier transform (STFT) has been used as a feature extraction algorithm. A novel combination between Kalman filter and tree-structured, as well as self-organizing map (SOM) neural network has been proposed as an estimator and classifier, respectively. The results show that the proposed approach is overperformed to the conventional classifier SVM by 1.6% and the gained classification is 92.6%, while the reduction error due to using Kalman filter is 95%.

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Micro-Doppler based detection and tracking of UAVs with multistatic radar

Cited by 149 publications

References 10 publications

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

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

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

Moving Target Tracking and Classification Based on Micro-Doppler Signatures

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