Convolutional Neural Networks for Classification of Drones Using Radars

Raval, Divy; Hunter, Emily; Hudson, Sinclair; Damini, Anthony; Balaji, Bhashyam

doi:10.3390/drones5040149

Cited by 14 publications

(10 citation statements)

References 34 publications

(49 reference statements)

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…Specific parts of an object that move separately from the rest provide a micro-Doppler signature. These signatures may be produced by drones simply by rotating their propeller blades [ 31 ]. As well, the drone’s moving components, such as rotors or wings, produce distinctive radar echoes known as micro-Doppler signatures.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…The authors of [ 33 ] demonstrated that drones and birds may be consistently distinguished by analyzing the radar return, including the micro-Doppler characteristic. Further, the work [ 31 ] addresses the problem of classifying various drone types such as DJI and Parrot using radar signals at X- and W-band frequencies, Convolutional neural networks (CNNs) were used to analyze the short-time Fourier transform (STFT) spectrograms of the simulated radar signals emitted by the drones. The experimental results demonstrated that a neural network that was trained using data from an X-band radar with a 2 kHz pulse repetition frequency outperformed a CNN trained using the aforementioned W-band radar.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

See 2 more Smart Citations

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A deeper evaluation is carried out by evaluating the the F1-score for each configuration, by varying the SNR, the parameter variation span, and the time on-target in the test. The F1-score is defined as [23]:…”

Section: Target Classmentioning

confidence: 99%

Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis

Leonardi

Ligresti

Piracci³

2022

Drones

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The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a time–frequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the drone’s micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present.

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“…However, there is also a disadvantage in that the explainability of the classification result is low. Classification using micro-Doppler signatures has been currently being studied more actively for application to the motion of drones and humans than to application to space targets [19][20][21][22][23][24]. This is because drones and radar technologies have been expanded to the private sector and are attracting attention.…”

Section: Introductionmentioning

confidence: 99%

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle

Lee

Kim

Min

et al. 2022

Sensors

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Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures. Previous studies have only used micro-Doppler signatures such as spectrogram and cadence velocity diagram (CVD), but in this paper, we propose a method to generate micro-Doppler signatures taking into account the relative incident angle that a radar can obtain during the target tracking process. The AlexNet and ResNet-18 networks, which are representative convolutional neural network architectures, are transfer-learned using two types of datasets constructed using the proposed and conventional signatures to classify six classes of space targets and a debris–cone, rounded cone, cone with empennages, cylinder, curved plate, and square plate. Among the proposed signatures, the spectrogram had lower classification accuracy than the conventional spectrogram, but the classification accuracy increased from 88.97% to 92.11% for CVD. Furthermore, when recalculated not with six classes but simply with only two classes of precessing space targets and tumbling debris, the proposed spectrogram and CVD show the classification accuracy of over 99.82% for both AlexNet and ResNet-18. Specially, for two classes, CVD provided results with higher accuracy than the spectrogram.

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Convolutional Neural Networks for Classification of Drones Using Radars

Cited by 14 publications

References 34 publications

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle

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