Drone detection and radar‐cross‐section measurements by
            <b>RAD‐DAR</b>

Quevedo, Álvaro Duque de; Urzaiz, Fernando Ibañez; Menoyo, Javier Gismero; López, A.

doi:10.1049/iet-rsn.2018.5646

Cited by 28 publications

(14 citation statements)

References 14 publications

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“…This section summarises the main aspects of the sensor used to collect the data: a 2D ubiquitous or persistent radar system developed by the microwave and radar group from the Universidad Politecnica de Madrid [23, 24] called RAD–DAR (Radar with Digital Array Receiver). Ubiquitous radars are those that can light up the surveillance scene all the time with a wide beam.…”

Section: Radar Systemmentioning

confidence: 99%

DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

Roldan

del-Blanco

Quevedo

et al. 2020

IET Radar, Sonar & Navigation

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Section: Radar Systemmentioning

confidence: 99%

DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

Roldan

del-Blanco

Quevedo

et al. 2020

IET Radar, Sonar & Navigation

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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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“…In [ 67 ] a persistent X-band FMCW radar is considered, which constantly illuminates the area under observation. The radar system consists of a transmitting antenna and eight receiving antennas, all in microstrip technology.…”

Section: Literature On Drone Detectionmentioning

confidence: 99%

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Coluccia

Parisi

Fascista

2020

Sensors

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Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

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Drone detection and radar‐cross‐section measurements by RAD‐DAR

Cited by 28 publications

References 14 publications

DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

DopplerNet: a convolutional neural network for recognising targets in real scenarios using a persistent range–Doppler radar

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

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