35 GHz FMCW drone detection system

Drozdowicz, Jędrzej; Wielgo, Maciej; Samczyński, Piotr; Kulpa, Krzysztof; Krzonkalla, Jarosław; Mordzonek, Maj; Bryl, Marcin; Jakielaszek, Zbigniew

doi:10.1109/irs.2016.7497351

Cited by 112 publications

(62 citation statements)

References 3 publications

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“…The researchers applied acoustics, radar and lidar to monitor a wide azimuthal area (360°) and to simultaneously track multiple UAVs, and optical sensors for sequential identification with a very narrow field of view. In [ 9 ] the team presents an experimental system dedicated for the detection and tracking of small aerial targets such as unmanned aerial vehicles (UAVs) in particular small drones (multi-rotors). A system for acoustic detection and tracking of small objects in movement, such as UAVs or terrestrial robots, using acoustic cameras is introduced in [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

Development of an Acoustic System for UAV Detection

Dumitrescu

Minea

Costea

et al. 2020

Sensors

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The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.

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Section: Related Workmentioning

confidence: 99%

Development of an Acoustic System for UAV Detection

Dumitrescu

Minea

Costea

et al. 2020

Sensors

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“…The implementation of radar technology is discussed in [28] for AmDr detection. In [29], an RF based experimental setup is developed for AmDr detection. Radio access network based AmDr detection is performed in [30].…”

Section: Ref No Year Of Publicationmentioning

confidence: 99%

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Uddin

Altaf

Bilal

et al. 2020

Computer Communications

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Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. Am-Drs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in * sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbor (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

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“…Also, they are flying at relatively much lower velocities, which decreases the Doppler signature [15,16]. Even though such examples as [11,15,17,18] exist, especially in K, S, X-band and with the exploitation of Doppler effect, generally they fail to classify other aerial objects such as birds and the background clutter due their increased sensitivity for this particular case [16]. Hence, RADAR technology has not been considered as an effective solution counter drones, especially for autonomous configurations.…”

Section: Drone Detection and Trackingmentioning

confidence: 99%

Deep learning-based strategies for the detection and tracking of drones using several cameras

Unlu

Zenou

Rivière

et al. 2019

IPSJ T Comput Vis Appl

119

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Commercial Unmanned aerial vehicle (UAV) industry, which is publicly known as drone, has seen a tremendous increase in last few years, making these devices highly accessible to public. This phenomenon has immediately raised security concerns due to fact that these devices can intentionally or unintentionally cause serious hazards. In order to protect critical locations, the academia and industry have proposed several solutions in recent years. Computer vision is extensively used to detect drones autonomously compared to other proposed solutions such as RADAR, acoustics and RF signal analysis thanks to its robustness. Among these computer vision-based approaches, we see the preference of deep learning algorithms thanks to their effectiveness. In this paper, we are presenting an autonomous drone detection and tracking system which uses a static wide-angle camera and a lower-angle camera mounted on a rotating turret. In order to use memory and time efficiently, we propose a combined multi-frame deep learning detection technique, where the frame coming from the zoomed camera on the turret is overlaid on the wide-angle static camera's frame. With this approach, we are able to build an efficient pipeline where the initial detection of small sized aerial intruders on the main image plane and their detection on the zoomed image plane is performed simultaneously, minimizing the cost of resource exhaustive detection algorithm. In addition to this, we present the integral system including tracking algorithms, deep learning classification architectures and the protocols.

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35 GHz FMCW drone detection system

Cited by 112 publications

References 3 publications

Development of an Acoustic System for UAV Detection

Development of an Acoustic System for UAV Detection

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Deep learning-based strategies for the detection and tracking of drones using several cameras

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