Numerical and experimental radar cross section analysis of the quadrocopter DJI Phantom 2

Schröder, Arne; Renker, Matthias; Aulenbacher, Uwe; Murk, Axel; Böniger, Urs; Oechslin, R.; Wellig, Peter

doi:10.1109/radarconf.2015.7411928

Cited by 48 publications

(37 citation statements)

References 7 publications

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“…In a recent publication we have investigated the variation of the RCS of microdrones and their blades as a function of frequency and polarization through simulations and experiments in the controlled environment of an anechoic chamber [13]. In [14] the analysis of RCS of a common model of quadcopter (DJI Phantom 2) was presented using numerical simulations of electromagnetic models with different degrees of fidelity in reproducing the physical parts of the simulated small UAV, and compared with the experimental data obtained.…”

Section: Introductionmentioning

confidence: 99%

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Ritchie

Fioranelli

Borrion

et al. 2017

IET Radar, Sonar & Navigation

132

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This paper presents the use of micro-Doppler signatures collected by a multistatic radar to detect and discriminate between micro-drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the Radar Cross Section of the micro-drones, as well as the Singular Value Decomposition (SVD) and centroid of the micro-Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid-based features and multistatic information. For the non-hovering scenarios classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.

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Section: Introductionmentioning

confidence: 99%

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Ritchie

Fioranelli

Borrion

et al. 2017

IET Radar, Sonar & Navigation

132

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show abstract

“…Since most sUAV parts are made of carbon fiber, plastic and styrofoam, this class of drones is characterized by a very small radar cross section (RCS) between -30dBsm and -10dBsm (Schroder et al, 2015) (Chenchen and Hao, 2016). SUAVs exhibit a characteristic micro-Doppler signature that can be used for detection and for discrimination from avian targets such as birds (Werth, 2016).…”

Section: Introductionmentioning

confidence: 99%

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Vos¹,

Leijnse²,

Uijlenhoet³

2018

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“…Previous work in [2] and [3] investigated the changes in RCS of a micro-drone and its blades through numerical simulations and experiments in a controlled environment. An important challenge to address for effective radar detection of micro-drones is the possibility of discriminating them from birds to avoid significant false alarm rates.…”

Section: Introductionmentioning

confidence: 99%

Monostatic and bistatic radar measurements of birds and micro-drone

Ritchie

Fioranelli

Griffiths

et al. 2016

2016 IEEE Radar Conference (RadarConf)

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-This paper analyses the experimental results from recent monostatic and bistatic radar measurements of multiple birds as well as a quadcopter micro-drone. The radar system deployed for these measurements was the UCL developed NetRAD system. The aim of this work is to evaluate the key differences observed by a radar system between different birds and a micro-drone. In order to do simultaneous monostatic co and cross polarized data was measured along with coherent bistatic measurements. The results obtained show comparable signature within the time domain and a marked difference in the Doppler domain, from the various birds in comparison to the micro-drone.

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Numerical and experimental radar cross section analysis of the quadrocopter DJI Phantom 2

Cited by 48 publications

References 7 publications

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Monostatic and bistatic radar measurements of birds and micro-drone

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