research-article

DronePrint: Acoustic Signatures for Open-set Drone Detection and Identification with Online Data

Authors:

Harini Kolamunna,

Thilini Dahanayaka,

Suranga Seneviratne,

Kanchana Thilakaratne,

Albert Y. Zomaya,

Aruna SeneviratneAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 1

Article No.: 20, Pages 1 - 31

https://doi.org/10.1145/3448115

Published: 30 March 2021 Publication History

Abstract

With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.

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Mejia-Herrera MBotero-Valencia JOrtega JHernández-García R(2025)Development of a Solar-Powered Edge Processing Perimeter Alert System with AI and LoRa/LoRaWAN Integration for Drone Detection and Enhanced SecurityDrones10.3390/drones90100439:1(43)Online publication date: 10-Jan-2025
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Index Terms

DronePrint: Acoustic Signatures for Open-set Drone Detection and Identification with Online Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Neural networks

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 1

March 2021

1272 pages

EISSN:2474-9567

DOI:10.1145/3459088

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https://doi.org/10.3390/drones9010043
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https://dl.acm.org/doi/10.1109/TIFS.2025.3527814
Famili AStavrou AWang HPark JGerdes R(2024)Securing Your Airspace: Detection of Drones Trespassing Protected AreasSensors10.3390/s2407202824:7(2028)Online publication date: 22-Mar-2024
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