Acoustic-Based UAV Detection Using Late Fusion of Deep Neural Networks

  1. Casabianca, Pietro
  2. Zhang, Yu
  3. Diego González Aguilera ed. lit. 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Journal:
Drones

ISSN: 2504-446X

Year of publication: 2021

Volume: 5

Issue: 3

Pages: 54

Type: Article

DOI: 10.3390/DRONES5030054 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Drones

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Metrics

Cited by

  • Web of Science Cited by: 9 (09-09-2023)
  • Dimensions Cited by: 7 (30-03-2023)

JCR (Journal Impact Factor)

  • Year 2021
  • Journal Impact Factor: 5.532
  • Journal Impact Factor without self cites: 4.848
  • Article influence score: 0.89
  • Best Quartile: Q2
  • Area: REMOTE SENSING Quartile: Q2 Rank in area: 10/34 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2021
  • SJR Journal Impact: 0.995
  • Best Quartile: Q1
  • Area: Information Systems Quartile: Q1 Rank in area: 82/380
  • Area: Computer Science Applications Quartile: Q1 Rank in area: 171/791
  • Area: Aerospace Engineering Quartile: Q1 Rank in area: 21/140
  • Area: Control and Systems Engineering Quartile: Q1 Rank in area: 66/281
  • Area: Artificial Intelligence Quartile: Q2 Rank in area: 79/286

Scopus CiteScore

  • Year 2021
  • CiteScore of the Journal : 7.2
  • Area: Aerospace Engineering Percentile: 91
  • Area: Computer Science Applications Percentile: 83
  • Area: Information Systems Percentile: 83
  • Area: Control and Systems Engineering Percentile: 82
  • Area: Artificial Intelligence Percentile: 76

Journal Citation Indicator (JCI)

  • Year 2021
  • Journal Citation Indicator (JCI): 0.94
  • Best Quartile: Q2
  • Area: REMOTE SENSING Quartile: Q2 Rank in area: 18/57

Dimensions

(Data updated as of 30-03-2023)
  • Total citations: 7
  • Recent citations: 7
  • Field Citation Ratio (FCR): 5.43

Abstract

Multirotor UAVs have become ubiquitous in commercial and public use. As they become more affordable and more available, the associated security risks further increase, especially in relation to airspace breaches and the danger of drone-to-aircraft collisions. Thus, robust systems must be set in place to detect and deal with hostile drones. This paper investigates the use of deep learning methods to detect UAVs using acoustic signals. Deep neural network models are trained with mel-spectrograms as inputs. In this case, Convolutional Neural Networks (CNNs) are shown to be the better performing network, compared with Recurrent Neural Networks (RNNs) and Convolutional Recurrent Neural Networks (CRNNs). Furthermore, late fusion methods have been evaluated using an ensemble of deep neural networks, where the weighted soft voting mechanism has achieved the highest average accuracy of 94.7%, which has outperformed the solo models. In future work, the developed late fusion technique could be utilized with radar and visual methods to further improve the UAV detection performance.

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