UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models

Matson, Eric T.; Yang, Bowon; Smith, Anthony V. W.; Dietz, Eric; Gallagher, John C.

doi:10.1109/irc.2019.00103

Cited by 52 publications

(39 citation statements)

References 26 publications

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“…Several spectrograms and filters are applied to the input sound to perform feature detection before feeding the concurrent neural networks architecture to perform the task. The scientific literature is still rich with regard to the recognition of the acoustic signature of UAVs [53]- [55]. Other research works use different features to detect the UAV presence such as the WiFi traffic [56] or the UAV radio frequency signal [57]- [60].…”

Section: ) Uav Detectionmentioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

103

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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

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Section: ) Uav Detectionmentioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

103

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“…These models showed high accuracy on test data (>90%). An efficient drone detection model using the Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) for noise detection is discussed in [126]. Unlike [67], the authors of [126] use SVM and CNN for drone detection as compared to LSTM approach used in [67].…”

Section: Applications Of Machine Learning For Drone Communication Sec...mentioning

confidence: 99%

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Hassija,

Chamola,

Agrawal

et al. 2021

Preprint

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Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

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“…Support vector machines (SVM) and convolutional neural networks (CNN) were trained with the data collected in person. The purpose was to determine the ability of this setup to track trajectories of flying drones [ 23 ]. In noisy environments, sound signature of UAVs is more difficult to recognize.…”

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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UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models

Abstract: This paper introduced a near real-time acoustic unmanned aerial vehicle detection system with multiple listening nodes using machine learning models. An audio dataset was

Cited by 52 publications

References 26 publications

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Development of an Acoustic System for UAV Detection

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