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Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals

Author

Listed:
  • Zahoor Uddin

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
    These authors contributed equally to this work.)

  • Aamir Qamar

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
    These authors contributed equally to this work.)

  • Abdullah G. Alharbi

    (Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 42421, Saudi Arabia
    These authors contributed equally to this work.)

  • Farooq Alam Orakzai

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
    These authors contributed equally to this work.)

  • Ayaz Ahmad

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
    These authors contributed equally to this work.)

Abstract

Detection of unauthorized drones is mandatory for defense organizations and also for human life protection. Currently, detection methods based on thermal, video, radio frequency (RF) and acoustic signals exist. In previous research, we presented an acoustic signals-based multiple drones detection technique utilizing independent component analysis (ICA) in the presence of interfering sources. In this paper, a method is proposed in which the mixed signals are first separated taking the ICA technique into account. After extracting the features, the support vector machines (SVM) and the k-nearest neighbors (KNN) are used to identify multiple drones in the field. This technique can detect multiple drones in static and quasi-static mixing scenarios, while failing in time-varying scenarios. In this paper, a time-varying drone detection technique (TVDDT) is proposed that first stores a data set of the mixed signals in a time-varying scenario, where time variations occur within the processing data blocks. After estimating the mixing matrices, we developed a technique to track variations in the channel. This technique is based on variations in the mixing coefficients. The proposed channel tracking technique performs classification and detection based on minimum variation criteria in the channel. The proposed TVDDT technique is evaluated through simulations and its superior performance is observed.

Suggested Citation

  • Zahoor Uddin & Aamir Qamar & Abdullah G. Alharbi & Farooq Alam Orakzai & Ayaz Ahmad, 2022. "Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4041-:d:782305
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    References listed on IDEAS

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    1. Jonas Harvard & Mats Hyvönen & Ingela Wadbring, 2020. "Journalism from Above: Drones and the Media in Critical Perspective," Media and Communication, Cogitatio Press, vol. 8(3), pages 60-63.
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