Analysis of the radar return of micro-UAVs in flight

Harman, Stephen

doi:10.1109/radar.2017.7944379

Cited by 22 publications

(16 citation statements)

References 6 publications

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“…QinetiQ [31], explored the specific Doppler patterns of various drones during flight, this was done with their X Band FMCW radar, named Obsidian. A very interesting aspect of their analysis was the inclusion of a Doppler spectrum produced by the DJI Phantom, to which each contributing component is clarified clearly (Fig.…”

Section: Drone Detection and Classification Techniquesmentioning

confidence: 99%

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Review of radar classification and RCS characterisation techniques for small UAVs or drones

Patel

Fioranelli

Anderson

2018

IET Radar, Sonar & Navigation

151

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This review explores radar based techniques currently utilised in literature to monitor small UAVs or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration is briefly outlined. Key difficulties involved in the identification and hence tracking of these 'radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and RCS characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum.

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Section: Drone Detection and Classification Techniquesmentioning

confidence: 99%

“…DJI Phantom 2 Doppler spectrum signature, showing selected bands of interest, Courtesy of S. Harman (Qinetiq)[31] …”

mentioning

confidence: 99%

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Patel

Fioranelli

Anderson

2018

IET Radar, Sonar & Navigation

151

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“…The Doppler modulation spectrum (DMS) of the target represents the Doppler frequency distribution and the amplitude of each frequency component of the target’s echo signal. Frequency components of the DMS consist of the main frequency and micro-Doppler side frequencies [17]. The main frequency component is the main Doppler frequency shift induced by the main motion of the target’s body.…”

Section: Multi-dimensional Feature Analysismentioning

confidence: 99%

“…The experimental results show that the improved Doppler resolution provides more useful information, which is conducive to the classification of small UAVs. In [15,17], the QinetiQ company used X-band FMCW radar to explore specific Doppler patterns of various UAVs during flight, proving that the unique Doppler plot of the target is an important prospect for target identification in future studies. In [18], the authors incorporated different micro-Doppler-based classification techniques to classify warheads and confusing objects.…”

Section: Introductionmentioning

confidence: 99%

A Complete Automatic Target Recognition System of Low Altitude, Small RCS and Slow Speed (LSS) Targets Based on Multi-Dimensional Feature Fusion

Chen

et al. 2019

Sensors

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Low altitude, small radar cross-section (RCS), and slow speed (LSS) targets, for example small unmanned aerial vehicles (UAVs), have become increasingly significant. In this paper, we propose a new automatic target recognition (ATR) system and a complete ATR chain based on multi-dimensional features and multi-layer classifier system using L-band holographic staring radar. We consider all steps of the processing required to make a classification decision out of the raw radar data, mainly including preprocessing for the raw measured Doppler data including regularization and main frequency alignment, selection, and extraction of effective features in three dimensions of RCS, micro-Doppler, and motion, and multi-layer classifier system design. We design creatively a multi-layer classifier system based on directed acyclic graph. Helicopters, small fixed-wing, and rotary-wing UAVs, as well as birds are considered for classification, and the measured data collected by L-band radar demonstrates the effectiveness of the proposed complete ATR classification system. The results show that the ATR classification system based on multi-dimensional features and k-nearest neighbors (KNN) classifier is the best, compared with support vector machine (SVM) and back propagation (BP) neural networks, providing the capability of correct classification with a probability of around 97.62%.

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“…There are two kinds of UAV which are the copter UAV and the fixed‐wing UAV. For the copter UAVs, many investigations have been carried out to analyse their characteristics including the RCS [1–5], Doppler spectrum [6–8], micro‐Doppler [9–15], polarimetry [16, 17] and ISAR images [1, 18]. It is well known that the RCS of UAV is influenced by the radar operating parameters such as radar carrier frequency, polarisation mode and observation angle.…”

Section: Introductionmentioning

confidence: 99%