Robust Drone Detection for Acoustic Monitoring Applications

Ohlenbusch, Mattes; Ahrens, Aike; Rollwage, Christian; Bitzer, Jöerg

doi:10.23919/eusipco47968.2020.9287433

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…[125] A database comprising 7001 drone flight observations and 3818 noise recording observations in a regulated setting where signals are played 1 at a time was created and each observation consisted of a 0.2 s signal sequence. [126] UFive different drone models are included in the training materials: the ALIGN M690L Multi-Drone, the SKY-HERO Little Spyder, the DJI Phantom4, the DJI Mavic, and a custom-built racing drone. Several UAV types that were absent from training dataset recordings are included in the testing material (DJI F450, Unique Taifun H520).…”

Section: Reference Datasets Informationmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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Section: Reference Datasets Informationmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…Nevertheless, the sound produced by propeller blades is frequently employed for detection because it has a comparatively larger amplitude. Numerous research works have examined the sound produced by drones, using characteristics like frequency, amplitude, modulation, and duration to identify a drone’s existence [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ].…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…In future works, the authors plan to improve detection accuracy by utilizing deep neural networks with a huge amount of acoustic data. A robust ML-based drone detection framework for acoustic surveillance was established in [ 53 ]. The dataset was captured in various sound environments and included various UAV types for training and unseen test sets, as well as sounds of ambient noises such as birds, wind, engines, and building noise.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

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Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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“…Once obtained, the MFCC or spectragram feature set can be used to train Long-Short Term Memory (LSTM) models [24], or Convolution type models such as CNNs [31]- [36], Recurrent Neural Networks (RNNs) [32]- [34] which incorporate temporal dependence and Convolutional-RNNs (CRNNs) [33], [34]. The feature set can also be used to train vector type models including, Support Vector Machines (SVMs) [27], [28], [30], [35], Gaussian Mixture Models [32] and KNNs [37] or, retrain existing models such as random forests [38], ResNet [25] and LeNet [39].…”

Section: Introductionmentioning

confidence: 99%

DroneSense: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks

et al. 2022

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The growing ubiquity of drones has raised concerns over the ability of traditional air-space monitoring technologies to accurately characterise such vehicles. Here, we present a CNN using a decision tree and ensemble structure to fully characterise drones in flight. Our system determines the drone type, orientation (in terms of pitch, roll, and yaw), and performs segmentation to classify different body parts (engines, body, and camera). We also provide a computer model for the rapid generation of large quantities of accurately labelled photo-realistic training data and demonstrate that this data is of sufficient fidelity to allow the system to accurately characterise real drones in flight. Our network will provide a valuable tool in the image processing chain where it may build upon existing drone detection technologies to provide complete drone characterisation over wide areas.

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Robust Drone Detection for Acoustic Monitoring Applications

Cited by 11 publications

References 19 publications

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

DroneSense: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks

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