A study of real-time recognition of unmanned aerial vehicles in outdoor areas based on a random forest algorithm
Abstract
With the widespread use of unmanned aerial vehicles (UAVs) in life, the real-time recognition of UAVs has become an important issue. The authors of this paper mainly studied the application of the random forest (RF) algorithm in the outdoor real-time recognition of UAVs. Mel-Frequency Cepstral Coefficient (MFCC) features were extracted from sound signals firstly, and then the RF method was combined with weighted voting to obtain the improved random forest (IRF) method to identify UAV sounds and environmental sounds. An experimental analysis was conducted. The modeling time of the IRF method increased by 9.52% compared with the RF method, and the recognition rate of the IRF method decreased with the increase of the distance from the microphone; however, the recognition rate of the IRF method was always higher than that of the RF method, and the recognition rate of the IRF method for the mixed samples was always higher than 90%. When the distance was 10 m, the IRF method still had a recognition rate of 91.29%. The experimental results verify the effectiveness of the IRF method for the outdoor real-time recognition of UAVs and its practical application feasibility.
Keyword : random forest, unmanned aerial vehicle, sound signal, voting mechanism
How to Cite
Tian, Z., & Yu, D. (2022). A study of real-time recognition of unmanned aerial vehicles in outdoor areas based on a random forest algorithm. Aviation, 26(4), 169–175. https://doi.org/10.3846/aviation.2022.17910
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Pawar, M. D., & Kokate, R. D. (2021). Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients. Multimedia Tools and Applications, 80(10), 15563–15587. https://doi.org/10.1007/s11042-020-10329-2
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Wu, E. Q., Zhou, M. C., Hu, D. W., Zhu, L. J., Tang, Z. R., Qiu, X. Y., Deng, Y. P., Zhu, L. M., & Ren, H. (2020). Self-paced dynamic infinite mixture model for fatigue evaluation of pilots’ brains. IEEE Transactions on Cybernetics, 52(7), 1–16. https://doi.org/10.1109/TCYB.2020.3033005
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Zeng, Y. C., Chen, Y. Y., Mao, Y. H., & Xie, X. J. (2015). Mel frequency cepstrum coefficient extraction method based on empirical mode decomposition and combined spectrum of Fourier transform and Wigner distribution. Natural Science Journal of Xiangtan University, 132(5), 563–573.
Zha, C., Ding, X., Yu, Y., & Wang, X. (2017). Quaternion-based nonlinear trajectory tracking control of a quadrotor unmanned aerial vehicle. Chinese Journal of Mechanical Engineering, 30, 77–92. https://doi.org/10.3901/CJME.2016.1026.127
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