Drone movement classification based on deep learning using micro-Doppler signature images

Lee, Dong-Yeob; Lee, Jae-In; Seo, Dong-Wook

doi:10.5916/jamet.2021.45.4.213

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…[162] The datasets included the class of Air Trainer, Sky Walker, T-REX 550, QuadroXL, and OktoXL drones with simulated and measured training signals 506, 475, 956, 477, 480 and 570, 886, 1284, 841, 1370, respectively. [163] The dataset exhibited changes in elevation angle of 0 to 90 degrees at 1-degree step intervals and azimuth angle of 0 to 180 degrees at 5-degree step intervals. For every movement, a total of 6552 spectrogram images were created.…”

Section: Uav Classification Based On ML Using Radarmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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Section: Uav Classification Based On ML Using Radarmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…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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Countering Large-Scale Drone Swarm Attack by Efficient Splitting

Chen

Meng

Liu

et al. 2022

IEEE Trans. Veh. Technol.

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Drone movement classification based on deep learning using micro-Doppler signature images

Cited by 4 publications

References 9 publications

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

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

Countering Large-Scale Drone Swarm Attack by Efficient Splitting

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