A Method of Fast and Simultaneous Calibration of Many Mobile FMCW Radars Operating in a Network Anti-Drone System

Nowak, Aleksander; Naus, Krzysztof; Maksimiuk, D.

doi:10.3390/rs11222617

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…The problem of protection against the common occurrence of small unmanned aerial vehicles (UAV) in recent years has been addressed by Nowak et al [9]. UAV, popularly known as drones, are used to carry out many tasks, but they are mainly used for observation by both private individuals and professionals.…”

Section: Radar Imaging and Processingmentioning

confidence: 99%

Radar and Sonar Imaging and Processing

2020

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The 21 papers (from 61 submitted) published in the Special Issue “Radar and Sonar Imaging Processing” highlighted a variety of topics related to remote sensing with radar and sonar sensors. The sequence of articles included in the SI dealt with a broad profile of aspects of the use of radar and sonar images in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used.

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Section: Radar Imaging and Processingmentioning

confidence: 99%

Radar and Sonar Imaging and Processing

2020

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“…A radar that can analyze the micro-Doppler signatures is a traditional and effective tool for anti-drone systems [ 1 , 2 ]. In [ 4 ], frequency modulated continuous wave radars were used to detect mobile drones. However, it requires expensive devices to implement and may be inappropriate in crowded urban areas or those areas with complex background clutter because distinguishing drones from complex backgrounds is difficult at low altitudes [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Small Drones Detection Based on Pruned YOLOv4

Liu

Fan

Ouyang

et al. 2021

Sensors

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To address the threat of drones intruding into high-security areas, the real-time detection of drones is urgently required to protect these areas. There are two main difficulties in real-time detection of drones. One of them is that the drones move quickly, which leads to requiring faster detectors. Another problem is that small drones are difficult to detect. In this paper, firstly, we achieve high detection accuracy by evaluating three state-of-the-art object detection methods: RetinaNet, FCOS, YOLOv3 and YOLOv4. Then, to address the first problem, we prune the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Furthermore, to improve the accuracy of small drone detection, we implement a special augmentation for small object detection by copying and pasting small drones. Experimental results verify that compared to YOLOv4, our pruned-YOLOv4 model, with 0.8 channel prune rate and 24 layers prune, achieves 90.5% mAP and its processing speed is increased by 60.4%. Additionally, after small object augmentation, the precision and recall of the pruned-YOLOv4 almost increases by 22.8% and 12.7%, respectively. Experiment results verify that our pruned-YOLOv4 is an effective and accurate approach for drone detection.

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“…It requires input from some assisting sensor providing possible direction to potentially suspicious objects. Radars constitute the second most common technology for drone detecting systems [11][12][13][14][15][16][17][18][19][20]. The assets of the corresponding methods are indisputable.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement

Wojtanowski

Zygmunt

Drozd

et al. 2021

Sensors

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Widespread availability of drones is associated with many new fascinating possibilities, which were reserved in the past for few. Unfortunately, this technology also has many negative consequences related to illegal activities (surveillance, smuggling). For this reason, particularly sensitive areas should be equipped with sensors capable of detecting the presence of even miniature drones from as far away as possible. A few techniques currently exist in this field; however, all have significant drawbacks. This study addresses a novel approach for small (<5 kg) drones detection technique based on a laser scanning and a method to discriminate UAVs from birds. The latter challenge is fundamental in minimizing the false alarm rate in each drone monitoring equipment. The paper describes the developed sensor and its performance in terms of drone vs. bird discrimination. The idea is based on simple cross-polarization ratio analysis of the optical echo received as a result of laser backscattering on the detected object. The obtained experimental results show that the proposed method does not always guarantee 100 percent discrimination efficiency, but provides certain confidence level distribution. Nevertheless, due to the hardware simplicity, this approach seems to be a valuable addition to the developed anti-drone laser scanner.

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A Method of Fast and Simultaneous Calibration of Many Mobile FMCW Radars Operating in a Network Anti-Drone System

Cited by 8 publications

References 16 publications

Radar and Sonar Imaging and Processing

Radar and Sonar Imaging and Processing

Real-Time Small Drones Detection Based on Pruned YOLOv4

Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement

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