Detection of small UAV helicopters using micro-Doppler

Tahmoush, David

doi:10.1117/12.2054329

Cited by 24 publications

(20 citation statements)

References 5 publications

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“…In [5,[8][9][10]] the micro-Doppler signatures of different models of micro-drones collected using a continuous wave (CW) X-band radar have been analysed to differentiate between different models and between microdrones and large birds. The use of radar micro-Doppler signatures to identify a small helicopter has also been reported in [11]. In [12] other features extracted at tracking level rather than from micro-Doppler signatures have been proposed to classify micro-drones and distinguish them from other aircraft, birds, or atmospheric phenomena.…”

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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“…Other works in [8] investigated the changes in the Radar Cross Section (RCS) of a small UAV and its rotor blades through simulations and controlled experiments. [9] proposes the use of micro-doppler signatures to distinguish between UAV and birds. In this paper an approach for detecting and tracking a small UAV using the UCL multistatic radar system NetRAD is presented.…”

Section: Introductionmentioning

confidence: 99%

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

Hoffmann

Ritchie

Fioranelli

et al. 2016

2016 IEEE Radar Conference (RadarConf)

146

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Abstract-This paper presents an approach for detection and tracking a micro-UAV using the multistatic radar NetRAD. Experimental trials were performed using NetRAD allowing for analysis of real data to assess the difficulty of detection and tracking of a micro-UAV target. The UAV detection is based on both time domain and micro-Doppler signatures, in order to enhance the discrimination between ground clutter and UAV returns. This micro-Doppler based procedure is shown to improve the clutter/target discrimination, in comparison to a Doppler-shift based procedure. The tracking approach is able to compensate for the limited quality measurement generated by each bistatic pair by fusing the measurements available from multiple bistatic pairs.

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“…Figures 2, 3, 4, and 5 came from a DARPA funded SBIR acknowledged below. Some of the initial G-band micro-range micro-Doppler data is recently published [28] and only minimally investigated at this point. The animal data includes lions, tigers, and bears as well as smaller species like monkeys and farm animals such as horses and alpacas.…”

Section: Micro-range Micro-doppler Databasementioning

confidence: 98%