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

Coluccia, Angelo; Parisi, Gianluca; Fascista, Alessio

doi:10.3390/s20154172

Cited by 97 publications

(47 citation statements)

References 71 publications

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“…Sometimes drones do not require any link, since their mission is pre-programmed. An excellent review of the current methods to detect and classify drones remotely is provided in [28], where the authors underline the problems with detecting small drones at large distances and distinguishing them from other flying objects. In order to increase the effectiveness of anti-drone sensors, the whole variety of advanced data analysis methods are implemented, including artificial network and deep learning techniques [26,29].…”

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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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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“…To monitor the surrounding environment or detect targets with high reliability, distributed radars are deployed in intelligent vehicle systems and anti-drone systems [1][2][3] (and references therein). Furthermore, because radar imaging for such surveillance that is robust to harsh environmental conditions can be effectively achieved by exploiting frequency modulated continuous waveform multiple-input multiple-output (FMCW MIMO) radar with low implementation cost, the radar imaging with FMCW MIMO radar is extensively investigated [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning‐based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple‐input multiple‐output radars

Seo

Yang

Hong

et al. 2021

IET Radar Sonar & Navi

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Deep-learning-based radar imaging is developed with distributed frequency modulated continuous waveform multiple-input multiple-output (FMCW MIMO) radars in which a deep-learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deeplearning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning.

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“…An important application of the Doppler radar sensor is to search and locate an alive person under the rubble of collapsed building or behind a wall by detecting vital signs, such as heartbeats and breathing [10][11][12]. The fundamental supporting this work is that a moving target relative to the radar sensor can induce a frequency shift of the echo as a result of the well-known Doppler effect; additional movements of smaller parts of the target will result in additional modulation of the main Doppler frequency shift, known as the micro-Doppler effect, i.e., the micro-Doppler signature, which reflects the periodic kinetic characteristics of a moving object and can be used for target or activity recognition and classification [13][14][15]. The radar sensor offers advantages in searching for and locating survivors due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions, but there may be weaknesses in the process to deploy radar networks immediately and appropriately in disaster zones, especially those which are unknown or hard to reach [16].…”

Section: Introductionmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs), also known as drones, have been recognized as one of the revolutionary advances in the recent technological evolution, and are now experiencing new applications also in SAR missions [15,17,18]. This is mainly because: (1) drones can be put into action immediately without any loss of time, and obtain a rapid overview of the disaster situation by transferring surveillance and video data in real time;…”

Section: Introductionmentioning

confidence: 99%

Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

2021

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The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed.

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

Cited by 97 publications

References 71 publications

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

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

Transfer learning‐based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple‐input multiple‐output radars

Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

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