文章摘要
王威,安腾飞,欧建平.无人机被动音频探测和识别技术研究[J].声学技术,2018,37(1):89~93
无人机被动音频探测和识别技术研究
Research on audio detection and recognition of UAV
投稿时间:2017-05-20  修订日期:2017-07-26
DOI:10.16300/j.cnki.1000-3630.2018.01.016
中文关键词: 无人机  探测和识别  梅尔频率倒谱系数  高斯混合模型
英文关键词: unmanned aerial vehicle (UAV)  detection and identification  mel-frequency cepstral coefficients (MFCC)  Gaussian mixture model (GMM)
基金项目:国防973基金赞助项目(613XXX0301)
作者单位E-mail
王威 长沙理工大学计算机与通信工程学院综合交通运输大数据智能处理湖南省重点实验室, 湖南长沙 410114  
安腾飞 长沙理工大学计算机与通信工程学院综合交通运输大数据智能处理湖南省重点实验室, 湖南长沙 410114 anyuanjing@stu.csust.edu.cn 
欧建平 国防科技大学电子科学与工程学院, 湖南长沙 410073  
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中文摘要:
      为解决近场空域低、慢、小旋翼无人机的安全威胁,提出基于音频信号分析的无人机探测识别方法。该方法采用改进流程和参数的梅尔频率倒谱系数(Mel-Frequency Cepstral Coeffi-cients,MFCC)和其一阶差分作为无人机音频的特征参数,结合提出的多距离分段采集法,通过训练高斯混合模型(Gaussian Mixture Model,GMM),建立多特征的无人机音频“指纹库”,最后用特征匹配算法实现无人机的探测和识别。实验结果表明,所提出的方法在典型郊区环境中可实现150 m距离内无人机的探测和识别,识别率达到84.4%。
英文摘要:
      In order to solve the security threat from low, slow and small rotor UAV (unmanned aerial vehicle) in the near field airspace, a method of analyzing the UAV audio signal is proposed. In this method, the Mel-Frequency Cepstral Coefficients (MFCC) and its first-order difference with improved solving flowchart and optimized parameters are used as the characteristic parameters of the UAV audio signal. The multi-distance segmentation method is used to train Gaussian mixture model (GMM) for the establishment of multi-feature "fingerprint library" of UAV audio signals, and finally the UAV detection and identification is achieved with the feature matching algorithm. The experimental results show that the proposed method can realize the UAV detection and recognition within 150m distance in typical suburban environment with an accuracy rate of 84.4%.
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