Sound recognition method of an anti-UAV system based on a convolutional neural network

XUE Shan; LI Guang-qing; Lü Qiong-ying; MAO Yi-wei

doi:10.13374/j.issn2095-9389.2020.06.30.008

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Chinese Journal of Engineering > 2020 > 42(11): 1516-1524

XUE Shan, LI Guang-qing, Lü Qiong-ying, MAO Yi-wei. Sound recognition method of an anti-UAV system based on a convolutional neural network[J]. Chinese Journal of Engineering, 2020, 42(11): 1516-1524. doi: 10.13374/j.issn2095-9389.2020.06.30.008

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XUE Shan, LI Guang-qing, Lü Qiong-ying, MAO Yi-wei. Sound recognition method of an anti-UAV system based on a convolutional neural network[J]. Chinese Journal of Engineering, 2020, 42(11): 1516-1524. doi: 10.13374/j.issn2095-9389.2020.06.30.008

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Sound recognition method of an anti-UAV system based on a convolutional neural network

doi: 10.13374/j.issn2095-9389.2020.06.30.008

1.
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
2.
Chongqing Research Institute, Changchun University of Science and Technology, Chongqing 400000, China

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Corresponding author: E-mail: 1660348815@qq.com
Received Date: 2020-06-30
Publish Date: 2020-11-25

Abstract

Abstract

With the rapid growth of the UAV market, UAVs have been widely used in aerial photography, agricultural plant protection, power inspection, forest fire prevention, high-altitude fire fighting, emergency communication, and UAV logistics. However, “black flight” incidents of unlicensed flights and random flights frequently occur, which results in severe security risks to civil aviation airports, sensitive targets, and major activities. Moreover, owing to their characteristics of maneuverability, intelligent control, and low cost, UAVs can be easily used for criminal activities, which threatens public and national security. How to effectively detect UAVs and implement effective measures for UAVs, especially “black-flying” UAVs, is an active and difficult problem that needs to be urgently solved, and it is also an important research area in the field of anti-UAV systems. The research and development of anti-UAV systems is an important focus in national public security, and UAV identification is one of the key technologies in anti-UAV systems. Aiming at the problem of how to recognize UAVs, a sound-recognition method based on a convolutional neural network (CNN) was proposed. The UAV anti-jamming technology based on acoustic signals is not easily affected by an UAV size, shelter, ambient light, and ground clutter, and sound is an inherent attribute of UAVs, which is also applicable to UAVs in a radio-silence state. In this study, UAV sounds, bird sounds, and human voice within 100 m were collected and preprocessed; then the mel frequency cepstral coefficient and gammatone frequency cepstral coefficient eigenvalues were extracted. Support vector machine (SVM) and CNN models were designed to recognize UAV sounds and other sounds. The experimental results show that the SVM and CNN accuracies are 93.3% and 96.7%, respectively. To further verify the recognition ability of the designed CNN, it was tested on some Urbansound8K datasets, and its accuracy reached 90%. The experimental results show that a CNN is feasible for UAV recognition, and it has a better recognition performance than a SVM.
- UAV,
- voice detection,
- public security,
- MFCC eigenvalue,
- GFCC eigenvalue,
- convolution neural network

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References

[1]	陳唯實, 劉佳, 陳小龍, 等. 基于運動模型的低空非合作無人機目標識別. 北京航空航天大學學報, 2019, 45(4):687 Chen W S, Liu J, Chen X L, et al. Non-cooperative UAV target recognition in low-altitude airspace based on motion model. J Beijing Univ Aeron Astron, 2019, 45(4): 687
[2]	Bisio I, Garibotto C, Lavagetto F, et al. Blind detection: Advanced techniques for WiFi-based drone surveillance. IEEE Trans Veh Technol, 2018, 68(1): 938
[3]	全厚德, 唐志強, 孫慧賢, 等. 基于偽隨機線性調頻的雙序列跳頻通信方法. 華中科技大學學報: 自然科學版, 2019, 47(11):30 Quan H D, Tang Z Q, Sun H X, et al. Binary-sequence frequency hopping communication method based on pseudo-random linear frequency modulation. J Huazhong Univ Sci Technol Nat Sci Ed, 2019, 47(11): 30
[4]	Huang F Z, Zeng J F, Zhang Y, et al. Convolutional recurrent neural networks with multi-sized convolution filters for sound-event recognition. Mod Phys Lett B, 2020, 34(23): 2050235 doi: 10.1142/S0217984920502358
[5]	Kim J, Min K, Jung M, et al. Occupant behavior monitoring and emergency event detection in single-person households using deep learning-based sound recognition. Build Environ, 2020, 181: 107092 doi: 10.1016/j.buildenv.2020.107092
[6]	蘭紅, 方治嶼. 零樣本圖像識別. 電子與信息學報, 2020, 42(5):1188 doi: 10.11999/JEIT190485 Lan H, Fang Z Y. Recent advances in zero-shot learning. J Electron Inf Technol, 2020, 42(5): 1188 doi: 10.11999/JEIT190485
[7]	Rai A K, Senthilkumar R, Aswin K R. Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network. Microprocessors Microsystems, 2020, 76: 103096 doi: 10.1016/j.micpro.2020.103096
[8]	Sainath T N, Mohamed A R, Kingsbury B, et al. Deep convolutional neural networks for LVCSR // 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, 2013: 8614
[9]	謝躍, 梁瑞宇, 包永強, 等. 融合改進梅爾譜特征和深信念網絡的語音測謊算法. 聲學學報, 2019, 44(2):214 Xie Y, Liang R Y, Bao Y Q, et al. Deception detection with spectral features based on deep belief network. Acta Acustica, 2019, 44(2): 214
[10]	孟偲, 李陽剛, 張國強, 等. 基于支持向量機的飛行器多余物信號識別. 北京航空航天大學學報, 2020, 46(3):488 Meng C, Li Y G, Zhang G Q, et al. Signal recognition of loose particles inside aerobat based on support vector machine. J Beijing Univ Aeron Astron, 2020, 46(3): 488
[11]	張科, 蘇雨, 王靖宇, 等. 基于融合特征以及卷積神經網絡的環境聲音分類系統研究. 西北工業大學學報, 2020, 38(1):162 doi: 10.3969/j.issn.1000-2758.2020.01.020 Zhang K, Su Y, Wang J Y, et al. Environment sound classification system based on hybrid feature and convolutional neural network. J Northwestern Polytech Univ, 2020, 38(1): 162 doi: 10.3969/j.issn.1000-2758.2020.01.020
[12]	Dua M, Aggarwal R K, Biswas M. GFCC based discriminatively trained noise robust continuous ASR system for Hindi language. J Ambient Intell Human Comput, 2019, 10(6): 2301
[13]	Ali H, Tran S N, Benetos E, et al. Speaker recognition with hybrid features from a deep belief network. Neural Computing Appl, 2018, 29(6): 13 doi: 10.1007/s00521-016-2501-7
[14]	耿琪深, 王豐華, 金霄. 基于Gammatone濾波器倒譜系數與鯨魚算法優化隨機森林的干式變壓器機械故障聲音診斷. 電力自動化設備, 2020, 40(8):191 Geng Q S, Wang F H, Jin X. Mechanical fault sound diagnosis based on GFCC and random forest optimized by whale algorithm for dry type transformer. Electr Power Autom Equip, 2020, 40(8): 191
[15]	侯公羽, 許哲東, 劉欣, 等. 無數學模型的非線性約束單目標系統優化方法改進. 工程科學學報, 2018, 40(11):1402 Hou G Y, Xu Z D, Liu X, et al. Optimization method improvement for nonlinear constrained single objective system without mathematic models. Chin J Eng, 2018, 40(11): 1402
[16]	于仙毅, 巫江虹, 高云輝. 基于主成分分析與支持向量機的熱泵系統制冷劑泄漏識別研究. 化工學報, 2020, 71(7):3151 Yu X Y, Wu J H, Gao Y H. Research on refrigerant leakage identification for heat pump system based on PCA-SVM models. CIESC J, 2020, 71(7): 3151
[17]	吐松江·卡日, 高文勝, 張紫薇, 等. 基于支持向量機和遺傳算法的變壓器故障診斷. 清華大學學報:自然科學版, 2018, 58(7):623 Kari T, Gao W S, Zhang Z W, et al. Power transformer fault diagnosis based on a support vector machine and a genetic algorithm. J Tsinghua Univ Sci Technol, 2018, 58(7): 623
[18]	舒暢, 金瀟, 李自品, 等. 基于CEEMDAN的配電變壓器放電故障噪聲診斷方法. 高電壓技術, 2018, 44(8):2603 Shu C, Jin X, Li Z P, et al. Noise diagnosis method of distribution transformer discharge fault based on CEEMDAN. High Voltage Eng, 2018, 44(8): 2603
[19]	Tuttle J F, Blackburn L D, Powell K M. On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction. Comput Chem Eng, 2020, 141: 106990 doi: 10.1016/j.compchemeng.2020.106990
[20]	王向陽, 何嶺松, 王平江, 等. 基于VMD的銑刀破損檢測. 振動與沖擊, 2020, 39(16):135 Wang X Y, He L S, Wang P J, et al. Milling cutter breakage detection based on VMD. J Vib Shock, 2020, 39(16): 135
[21]	Gong W F, Chen H, Zhang Z H, et al. A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors, 2019, 19(7): 1693 doi: 10.3390/s19071693
[22]	王紅霞, 周家奇, 辜承昊, 等. 用于圖像分類的卷積神經網絡中激活函數的設計. 浙江大學學報:工學版, 2019, 53(7):1363 Wang H X, Zhou J Q, Gu C H, et al. Design of activation function in CNN for image classification. J Zhejiang Univ Eng Sci, 2019, 53(7): 1363
[23]	曾燕, 陳岳林, 蔡曉東. 結合全局與局部池化的深度哈希人臉識別算法. 西安電子科技大學學報: 自然科學版, 2018, 45(5):163 Zeng Y, Chen Y L, Cai X D. Face recognition algorithm for the deep hash combined with global and local pooling. J Xidian Univ Nat Sci, 2018, 45(5): 163
[24]	梁敏健, 崔嘯宇, 宋青松, 等. 基于HOG-Gabor特征融合與Softmax分類器的交通標志識別方法. 交通運輸工程學報, 2017, 17(3):151 doi: 10.3969/j.issn.1671-1637.2017.03.016 Liang M J, Cui X Y, Song Q S, et al. Traffic sign recognition method based on HOG-Gabor feature fusion and Softmax classifier. J Traffic Transportation Eng, 2017, 17(3): 151 doi: 10.3969/j.issn.1671-1637.2017.03.016
[25]	王昱皓, 武建文, 馬速良, 等. 基于核主成分分析-SoftMax的高壓斷路器機械故障診斷技術研究. 電工技術學報, 2020, 35(增刊1): 267 Wang Y H, Wu J W, Ma S L, et al. Mechanical fault diagnosis research of high voltage circuit breaker based on Kernel principal component analysis and SoftMax. Trans China Electrotech Soc, 2020, 35(Suppl 1): 267
[26]	李雙峰. TensorFlow Lite: 端側機器學習框架. 計算機研究與發展, 2020, 57(9):1839 doi: 10.7544/issn1000-1239.2020.20200291 Li S F. TensorFlow Lite: On-device machine learning framework. J Comput Res Dev, 2020, 57(9): 1839 doi: 10.7544/issn1000-1239.2020.20200291