Optical and acoustical UAV detection

Christnacher, Frank; Hengy, Sébastien; Laurenzis, Martin; Matwyschuk, Alexis; Naz, Pierre; Schertzer, Stéphane; Schmitt, Gwenaël

doi:10.1117/12.2240752

Cited by 53 publications

(34 citation statements)

References 2 publications

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“…In recent years, algorithms based on machine learning have been used to detect faults in machine functioning and control [50][51][52][53][54][55][56][57][58]. First, acoustic measurements were performed in an anechoic chamber and then these were analyzed to characterize the phenomenon [59][60][61][62][63][64][65]. An appropriate number of observations were then extracted, and these data were pre-processed.…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for UAV Blades Using Artificial Neural Network

2019

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In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of these devices has highlighted maintenance problems with regard to the propellers, which represent the source of propulsion of the aircraft. A defect in the propellers of a drone can cause the aircraft to fall to the ground and its consequent destruction, and it also constitutes a safety problem for objects and people that are in the range of action of the aircraft. In this study, the measurements of the noise emitted by a UAV were used to build a classification model to detect unbalanced blades in a UAV propeller. To simulate the fault condition, two strips of paper tape were applied to the upper surface of a blade. The paper tape created a substantial modification of the aerodynamics of the blade, and this modification characterized the noise produced by the blade in its rotation. Then, a model based on artificial neural network algorithms was built to detect unbalanced blades in a UAV propeller. This model showed high accuracy (0.9763), indicating a high number of correct detections and suggests the adoption of this tool to verify the operating conditions of a UAV. The test must be performed indoors; from the measurements of the noise produced by the UAV it is possible to identify an imbalance in the propeller blade.

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Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for UAV Blades Using Artificial Neural Network

2019

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“…Here, an active imaging device with a very narrow field of view could counter these challenges and could be used to gate out the background, increase resolution and signal-to-noise ratio, and be independent from lighting conditions. Our team has access to a shortwave-infrared laser gated-viewing system as presented by Christnacher et al 10 In the near future, we will perform tests to investigate the pose estimation approach in operational conditions. Lighting conditions and observing MUAV flight behavior at a larger range can reduce the ranging accuracy of the proposed pose estimation approach due to an expected smaller scaling effect of the MUAV with the range at narrower observation angles.…”

Section: Discussionmentioning

confidence: 99%

“…Detection and tracking of MUAVs have been studied using single millimeter-wave RADAR, 8 optical (VIS/SWIR) 9 sensors or within a multisensor approach using passive/active imaging, acoustics, and RADAR in various field trials. [10][11][12][13] Further, classification of MUAV has been investigated using image-based deep-learning and high-level data-fusion approaches. [14][15][16] Classification can also be used to distinguish MUAVs from, e.g., birds.…”

Section: Introductionmentioning

confidence: 99%

Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

2019

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The increasing number of microunmanned aerial vehicles (MUAVs) is a rising risk for personal privacy and security of sensitive areas. Owing to the highly agile maneuverability and small cross section of the MUAV, effective countermeasures (CMs) are hard to deploy, especially when a certain temporal delay occurs between the localization and the CM effect. Here, a reliable prediction of the MUAV flight behavior can increase the effectiveness of CMs. We propose a pose estimation approach to derive the three-dimensional (3-D) flight path from a stream of two-dimensional intensity images. The pose estimation in a single image results in an estimation of the current position and orientation of the quadcopter in 3-D space. Combined with flight behavior model, this information is used to reconstruct the flight path and to predict the flight behavior of the MUAV. In our laboratory experiments, we obtained a standard deviation between 1 and 24 cm in a five-frame prediction of the 3-D position, depending in the actual flight behavior.

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“…To increase UAV detection reliability, in [9], algorithms-based template matching and morphological filtering were adopted to improve the ability to recognize UAV within a wide range of relative distances. In Reference [10], acoustic and optical detectors were combined to improve UAV detection and tracking through active imaging.…”

Section: Introductionmentioning

confidence: 99%

Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

et al. 2021

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With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the most distinctive information to identify and classify targets in many radar systems. In this paper, we introduce an effective model for the micro-Doppler effect that is suitable for frequency-modulated continuous-wave (FMCW) radar applications, and exploit it to investigate UAV signatures. The latter depends on the number of UAV motors, which are considered vibrational sources, and their rotation speed. To demonstrate the reliability of the proposed model, it is used to build simulated FMCW radar images, which are compared with experimental data acquired by a 77 GHz FMCW multiple-input multiple-output (MIMO) cost-effective automotive radar platform. The experimental results confirm the model’s ability to estimate the class of the UAV, namely its number of motors, in different operative scenarios. In addition, the experimental results show that the motors rotation speed does not imprint a significant signature on the classification of the UAV; thus, the estimation of the number of motors represents the only viable parameter for small UAV classification using the micro-Doppler effect.

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Optical and acoustical UAV detection

Cited by 53 publications

References 2 publications

Fault Diagnosis for UAV Blades Using Artificial Neural Network

Fault Diagnosis for UAV Blades Using Artificial Neural Network

Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

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