UAV Classification with Deep Learning Using Surveillance Radar Data

Samaras, Stamatios; Magoulianitis, Vasileios; Dimou, Anastasios; Zarpalas, Dimitrios; Daras, Petros

doi:10.1007/978-3-030-34995-0_68

“…The radar signatures and CNN were used for distinguishing of UAVs from birds in [166]. Other literature using different neural network algorithms and radar data for the classification of UAV are available in [58], [59], [155], [167], [168].…”

Section: Classification Of Uavs Using Modern 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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“…The adjustment of transmission and reception parameters of the radar systems can be carried out cognitively using AI [92]. Furthermore, different AI algorithms are available for the detection and classification of an aerial vehicle [93], [94]. The stored database of features of different types of aerial vehicles and real-time features of a potential aerial vehicle are compared using AI algorithms for detection and classification.…”

Section: Signal Processing Techniquesmentioning

confidence: 99%

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

Khawaja¹

2022

Preprint

2

0

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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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“…Accuracy analysis Computational time analysis [11] 15, 25 SL 6 [12] 3.25 SL 2 [13] 5.725 SL 1 [14], [15] 8.75 SL 1 [16], [17] 26-40 SL 10 [18] 8-12 SL 2 [19] 9 SL 9 [20] 8-12 SL 1 [21] 8-10 SL 1 [22] 2.5 ML N/A [23] 10 ML 10 [24] 9.35 DL 5 [25] 8.75 DL 1 [26] N/A DL N/A Our work 15,25 SL, ML, DL 6 the signal-to-noise ratio (SNR). Further analysis is provided by means of the Monte Carlo analysis, with emphasis on low SNR conditions.…”

Section: # Of Uavsmentioning

confidence: 99%

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Ezuma¹,

Anjinappa²,

Semkin³

et al. 2021

Preprint

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This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-tonoise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.

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UAV Classification with Deep Learning Using Surveillance Radar Data

Cited by 15 publications

References 15 publications

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

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

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

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

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