Cristin-resultat-ID: 2168651
Sist endret: 14. desember 2023, 10:40
NVI-rapporteringsår: 2023
Resultat
Vitenskapelig artikkel
2023

Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning

Bidragsytere:
  • Wilson Ayyanthole Nelson
  • Ajit Jha
  • Abhinav Kumar og
  • Linga Reddy Cenkeramaddi

Tidsskrift

Journal of the Acoustical Society of America
ISSN 0001-4966
e-ISSN 1520-8524
NVI-nivå 2

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2023
Publisert online: 2023
Trykket: 2023
Volum: 154
Hefte: 1
Sider: 533 - 546

Importkilder

Scopus-ID: 2-s2.0-85165907077

Beskrivelse Beskrivelse

Tittel

Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning

Sammendrag

With the exponential growth in unmanned aerial vehicle (UAV)-based applications, there is a need to ensure safe and secure operations. From a security perspective, detecting and localizing intruder UAVs is still a challenge. It is even more challenging to accurately estimate the number of intruder UAVs on the scene. In this work, we propose a simple acoustic-based technique to detect and estimate the number of UAVs. Our method utilizes acoustic signals generated from the motion of UAV motors and propellers. Acoustic signals are captured by flying an arbitrary number of ten UAVs in different combinations in an indoor setting. The recorded acoustic signals are trimmed, processed, and arranged to create an UAV audio dataset. The UAV audio dataset is subjected to time-frequency transformations to generate audio spectrogram images. The generated spectrogram images are then fed to a custom lightweight convolutional neural network (CNN) architecture to estimate the number of UAVs in the scene. Following training, the proposed model achieves an average test accuracy of 93.33% as compared to state-of-the-art benchmark models. Furthermore, the deployment feasibility of the proposed model is validated by running inference time calculations on edge computing devices, such as the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier.

Bidragsytere

Wilson Ayyanthole Nelson

  • Tilknyttet:
    Forfatter
    ved Institutt for informasjons- og kommunikasjonsteknologi ved Universitetet i Agder

Ajit Jha

  • Tilknyttet:
    Forfatter
    ved Institutt for ingeniørvitenskap ved Universitetet i Agder

Abhinav Kumar

  • Tilknyttet:
    Forfatter
    ved Indian Institute of Technology Hyderabad

Linga Reddy Cenkeramaddi

  • Tilknyttet:
    Forfatter
    ved Institutt for informasjons- og kommunikasjonsteknologi ved Universitetet i Agder
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