Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals
Abstract
:1. Introduction
- Drone detection has been performed in previous works while considering the quasi-static channels. In this research, we concentrate on time-varying mixing channels to detect the unauthorized drones in a practical scenario. In order to achieve this objective, a channel tracking technique is proposed to track the channel variations in a time-varying scenario. The proposed technique is based on the estimated mixing matrices using the FastICA algorithm [42].
- The time varying drone detection (TVDDT) technique is proposed to detect single as well as multiple drones in the presence of strong interfering sources considering the time-varying scenario in which the drones are in motion.
- The detection of the drones is performed in a time-varying scenario considering the drones when the interfering sources are in motion. This becomes a challenging issue in drone detection utilizing audio signals.
2. The System Model
3. The Proposed TVDDT Technique
3.1. Time-Varying Scenario of the Flying Drones
3.2. The TVDDT Technique
Algorithm 1: The TVDDT algorithm. |
4. Simulation Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ICA | Independent component analysis |
Radio frequency | RF |
Support vector machines | SVM |
SNR | Signal-to-noise ratio |
Signal-to-interference ratio | |
K-nearest neighbors | KNN |
Time-varying drone detection technique | TVDDT |
Linear predictive cepstral coefficients | LPCC |
Mel-frequency cepstral coefficients | MFCC |
Power spectral density | PSD |
Data Block Length | Method | SVM-ICA | KNN-ICA |
---|---|---|---|
L = 10,000 | PSD | 92.57 | 97.9 |
RMS PSD | 96.1 | 99.1 | |
MFCC | 88.2 | 97.4 | |
L = 7000 | PSD | 91 | 97.2 |
RMS PSD | 94.9 | 98.3 | |
MFCC | 87.6 | 97 | |
L = 4000 | PSD | 90.3 | 96.7 |
RMS PSD | 94.1 | 98 | |
MFCC | 87.0 | 96.7 | |
L = 1000 | PSD | 89.7 | 96.0 |
RMS PSD | 93.3 | 97.1 | |
MFCC | 86.8 | 95.3 |
Data Block Length L | Method | SVM- ICA | KNN- ICA | SVM- TVDDT | KNN- TVDDT |
---|---|---|---|---|---|
L = 10,000 | PSD | 40.57 | 41.9 | 90 | 92.2 |
RMS PSD | 42.1 | 43.1 | 90.12 | 93.8 | |
MFCC | 38.2 | 42.4 | 83.21 | 92.13 | |
L = 7000 | PSD | 43 | 42.2 | 87 | 93.6 |
RMS PSD | 40.9 | 41.3 | 91 | 93.9 | |
MFCC | 38.6 | 39.53 | 84.2 | 93.1 | |
L = 4000 | PSD | 43.3 | 42.7 | 87.3 | 93.45 |
RMS PSD | 42.1 | 43.01 | 95.01 | 95.76 | |
MFCC | 40.0 | 41.7 | 85.1 | 94.01 | |
L = 1000 | PSD | 44.7 | 45.0 | 90.61 | 95.01 |
RMS PSD | 43.3 | 44.1 | 93.96 | 96.75 | |
MFCC | 40.8 | 42.3 | 86 | 95.35 |
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Uddin, Z.; Qamar, A.; Alharbi, A.G.; Orakzai, F.A.; Ahmad, A. Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals. Sustainability 2022, 14, 4041. https://doi.org/10.3390/su14074041
Uddin Z, Qamar A, Alharbi AG, Orakzai FA, Ahmad A. Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals. Sustainability. 2022; 14(7):4041. https://doi.org/10.3390/su14074041
Chicago/Turabian StyleUddin, Zahoor, Aamir Qamar, Abdullah G. Alharbi, Farooq Alam Orakzai, and Ayaz Ahmad. 2022. "Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals" Sustainability 14, no. 7: 4041. https://doi.org/10.3390/su14074041