Feature extraction for acoustic classification of small aircraft

Yakubovskiy, Alexander; Salloum, Hady; Sutin, Alexander; Sedunov, Alexander; Sedunov, Nikolay; Masters, David

doi:10.1109/waspaa.2015.7336911

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…The noise that is emitted by the UAV during flight is mainly caused by the rotational vibration of the propeller and engine. Furthermore, the signal is composed of a series of harmonics [5], which are influenced both by environmental factors and the UAV's own characteristics.…”

Section: Small Drone Audio Analysismentioning

confidence: 99%

Design and optimisation of microphone arrays based on acoustic positioning of small UAVs

Cao,

Wu,

Luo

et al. 2024

International Conference on Optoelectronic Materials and Devices (ICOMD 2023)

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Section: Small Drone Audio Analysismentioning

confidence: 99%

Design and optimisation of microphone arrays based on acoustic positioning of small UAVs

Cao,

Wu,

Luo

et al. 2024

International Conference on Optoelectronic Materials and Devices (ICOMD 2023)

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“…Localization and tracking of UAVs were studied with an acoustic array using calibration and beamforming [197]. In another study [198], a classifier with two layers was proposed, where the first layer determined the existence of a UAV and the second layer determined the UAV type, e.g., fixed-wing or rotary-wing. Machine learning-based algorithms such as the SVM and k-nearest neighbor (k-NN) algorithms, as well as neural networks were studied to classify the time-or frequencydomain acoustic/ultrasonic signals generated from UAVs [199], [200].…”

Section: ) Acoustic/ultrasonic Sensorsmentioning

confidence: 99%

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

et al. 2020

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Recognizing the various and broad range of applications of unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UAS) for personal, public and military applications, recent un-intentional malfunctions of uncontrollable UAVs or intentional attacks on them divert our attention and motivate us to devise a protection system, referred to as a counter UAV system (CUS). The CUS, also known as a counter-drone system, protects personal, commercial, public, and military facilities and areas from uncontrollable and belligerent UAVs by neutralizing or destroying them. This paper provides a comprehensive survey of the CUS to describe the key technologies of the CUS and provide sufficient information with wich to comprehend this system. The first part starts with an introduction of general UAVs and the concept of the CUS. In the second part, we provide an extensive survey of the CUS through a top-down approach: i) the platform of CUS including ground and sky platforms and related networks; ii) the architecture of the CUS consisting of sensing systems, command-and-control (C2) systems, and mitigation systems; and iii) the devices and functions with the sensors for detection-and-identification and localization-and-tracking actions and mitigators for neutralization. The last part is devoted to a survey of the CUS market with relevant challenges and future visions. From the CUS market survey, potential readers can identify the major players in a CUS industry and obtain information with which to develop the CUS industry. A broad understanding gained from the survey overall will assist with the design of a holistic CUS and inspire cross-domain research across physical layer designs in wireless communications, CUS network designs, control theory, mechanics, and computer science, to enhance counter UAV techniques further.INDEX TERMS Unmanned aerial vehicle (UAV), unmanned aircraft system (UAS), counter UAV system (CUS), counter drone systems, public safety, defense.

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“…Several studies suggested methods that triangulate sound obtained from centralized [138] and distributed [139] microphone arrays in order to detect a drone's direction of arrival and location. Another study [140] suggested a two-layer feature extractor that can be used to detect drones. While acoustic methods can be used to identify a drone and locate its presence (using multiple distributed microphones ), relying on acoustic signature methods for drone detection suffers from false negative detections due to the increasing number of drone models and false positive detections due to ambient noise.…”

Section: Acousticmentioning

confidence: 99%

SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps

Nassi¹,

Shabtai²,

Masuoka³

et al. 2019

Preprint

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The evolution of drone technology in the past nine years since the first commercial drone was introduced at CES 2010 has caused many individuals and businesses to adopt drones for various purposes. We are currently living in an era in which drones are being used for pizza delivery, the shipment of goods, and filming, and they are likely to provide an alternative for transportation in the near future. However, drones also pose a significant challenge in terms of security and privacy within society (for both individuals and organizations), and many drone related incidents are reported on a daily basis. These incidents have called attention to the need to detect and disable drones used for malicious purposes and opened up a new area of research and development for academia and industry, with a market that is expected to reach $1.85 billion by 2024. While some of the knowledge used to detect UAVs has been adopted for drone detection, new methods have been suggested by industry and academia alike to deal with the challenges associated with detecting the very small and fast flying objects. In this paper, we describe new societal threats to security and privacy created by drones, and present academic and industrial methods used to detect and disable drones. We review methods targeted at areas that restrict drone flights and analyze their effectiveness with regard to various factors (e.g., weather, birds, ambient light, etc.). We present the challenges arising in areas that allow drone flights, introduce the methods that exist for dealing with these challenges, and discuss the scientific gaps that exist in this area. Finally, we review methods used to disable drones, analyze their effectiveness, and present their expected results. Finally, we suggest future research directions and discuss whether the benefits from the decision to allow drones to fly in populated areas are actually worth the risk.

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Feature extraction for acoustic classification of small aircraft

Cited by 11 publications

References 9 publications

Design and optimisation of microphone arrays based on acoustic positioning of small UAVs

Design and optimisation of microphone arrays based on acoustic positioning of small UAVs

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps

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