Effective Ground-Truthing of Supervised Machine Learning for Drone Classification

Jacob, Sim; Jahangir, Mohammed; Fioranelli, Francesco; Baker, Chris; Dale, Holly

doi:10.1109/radar41533.2019.171322

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…Live radar trials with controlled targets form an important element of characterising radar performance, benchmarking system capabilities and collection of accurately truthed data to aid the development of machine learning classifiers ( [6,7]). Several small drones have been acquired that are routinely flown for data collection with control test targets.…”

Section: Field Trials Proceduresmentioning

confidence: 99%

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Jahangir,

Griffiths,

White

et al. 2023

IET Radar Sonar & Navi

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Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter.

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Section: Field Trials Proceduresmentioning

confidence: 99%

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Jahangir,

Griffiths,

White

et al. 2023

IET Radar Sonar & Navi

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“…Conventional ML classifiers that have been employed for micro-Doppler-based classification include linear and non-linear Support Vector Machines (SVM) [28,32,33,48,49], naïve Bayes [49,50], Maximum A Posteriori (MAP) [7], subspace reliability analysis [51], discriminant analysis [52], and decision trees [19,53]. These classifiers have achieved good results with classification accuracies of 90% and above on their acquired datasets under different experimental scenarios.…”

Section: Conventional ML Classification Algorithmsmentioning

confidence: 99%

Micro-Doppler Based Target Recognition With Radars: A Review

et al. 2022

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With the deployment of radar in versatile scenarios and a wide variety of potential targets, demand for automatic classification of various targets is increasing. The wide variety of radar signatures from physically smaller targets due to lower velocity / radar cross-section thresholds and the increased deployment of radar-based sensors do crowd the radar screen with misinterpreted targets. Micro-Doppler signatures have been widely employed by researchers for the recognition of those targets that exhibit micro-motions. This review article presents the evolution and recent advances in radar micro-Doppler based signature analysis and feature extraction. A review of the micro-Doppler-based target classification techniques along with their applications in defense and commercial sectors, has

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“…For the purpose of the spectrum data extraction, target location information is taken from the tracker output of the Gamekeeper 16U system. As detailed in [31], ground truth data is used to label the output tracks.…”

Section: Data Collection Overviewmentioning

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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Effective Ground-Truthing of Supervised Machine Learning for Drone Classification

Cited by 16 publications

References 8 publications

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Micro-Doppler Based Target Recognition With Radars: A Review

SNR‐dependent drone classification using convolutional neural networks

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