Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar

Bennett, C.; Jahangir, Mohammed; Fioranelli, Francesco; Ahmad, Bashar I.; Kernec, Julien Le

doi:10.1109/radarconf2043947.2020.9266702

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…In this plot the strong echo at zero Doppler is from stationary ground clutter and the dominant peak at 20-30 m/s is from the target body Doppler. Note there are micro-Doppler sidebands which are characteristic of a rotary wing target and this is consistent with previous published results with this type of radar [16]. Next is shown a range-Doppler plot for the beam that is centred on the target for one frame (see Fig.…”

Section: A Staring Radar Networksupporting

confidence: 89%

Advanced cognitive networked radar surveillance

Jahangir

Baker

Antoniou

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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The concept of a traditional monostatic radar with co-located transmit and receive antennas naturally imposes performance limits that can adversely impact applications. Using a multiplicity of transmit and receive antennas and exploiting spatial diversity provides additional degrees of design freedom that can help overcome such limitations. Further, when coupled with cognitive signal processing, such advanced systems offer significant improvement in performance over their monostatic counterparts. This will also likely lead to new applications for radar sensing. In this paper we explore the fundamentals of multistatic network radar highlighting both potential and constraints whilst identifying future research needs and applications. Initial experimental results are presented for a 2-node networked staring radar.

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Section: A Staring Radar Networksupporting

confidence: 89%

Advanced cognitive networked radar surveillance

Jahangir

Baker

Antoniou

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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“…In [182], a staring radar is used for distinguishing UAVs from birds. Staring radar operating at an Land and symmetrical peaks are extracted from the micro-Doppler signatures for UAV classification.…”

Section: Classification Of Aerial Vehicles Using Radar Systemsmentioning

confidence: 99%

“…Staring radar operating at an Land and symmetrical peaks are extracted from the micro-Doppler signatures for UAV classification. The symmetrical peak extraction algorithm can distinguish between UAVs and birds by focusing on the relationship between the components producing micro-Dopper and the main body of the aerial vehicle in [182]. In [183], AI enabled detection and classification of birds and UAVs is provided.…”

Section: Classification Of Aerial Vehicles Using Radar Systemsmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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“…In some reported publications, both kinematic and micro-Doppler features have been used together to enhance the classification performance [21,22]. In [23], the presence of symmetrical components about the body of the target was shown to be a key classification feature for distinguishing drones from birds.…”

Section: Introductionmentioning

confidence: 99%

SNR‐dependent drone classification using convolutional neural networks

Dale

Baker

Antoniou

et al. 2021

IET Radar Sonar & Navi

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Radar sensing offers a method of achieving 24-h all-weather drone surveillance, but in order to be maximally effective, systems need to be able to discriminate between birds and drones. This work examines drone-bird classification performance as a function of signal to noise ratio (SNR). Classification at low SNR values is necessary in order to classify drones with a small radar cross-section (RCS), as well as to facilitate reliable classification at longer ranges. To investigate the relationship between classification performance and SNR, Gaussian noise is added to an experimentally obtained dataset of radar spectrograms. Classification is performed by convolutional neural networks (CNNs). It is shown that for the data available classification accuracy drops with falling SNR, as might be expected for any given CNN. The degree to which performance degrades with reduced SNR is presented. It is further shown that simpler network architectures are more robust to noise. Finally, it is demonstrated that data augmentation can be used as a means of enhancing classification accuracy at lower SNR values. Bayesian optimisation is used to find the optimal augmentation hyperparameters and overall, classification accuracies of 92% are achieved at low SNR.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar

Cited by 15 publications

References 15 publications

Advanced cognitive networked radar surveillance

Advanced cognitive networked radar surveillance

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

SNR‐dependent drone classification using convolutional neural networks

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