| 段儒杰,行鸿彦,陈子正,刘 洋.基于被动音频的低小慢目标探测方法[J].电子测量与仪器学报,2021,35(10):41-47 |
| 基于被动音频的低小慢目标探测方法 |
| Detection method of low slow and small target based on passive audio |
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| DOI： |
| 中文关键词: 无人机探测 特征选择 灰狼优化算法 |
| 英文关键词:UAV detection feature selection gray wolf optimization algorithm |
| 基金项目:国家重点研发计划政府间重点专项（2021YFE0105500）、江苏省重点研发计划（BE2018719）、江苏省研究生科研与实践创新计划（SJCX20_0292）项目资助 |
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| Author | Institution |
| Duan Rujie | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science and Technology |
| Xing Hongyan | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science and Technology |
| Chen Zizheng | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science and Technology |
| Liu Yang | 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science and Technology |
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| 中文摘要: |
| 为了提高无人机的探测识别率,对无人机飞行声音进行时频域分析,针对无人机声音特性,将梅尔倒谱系数(MFCC)与
翻转梅尔倒谱系数 (IMFCC)组合来更好地表征无人机声音信号,新的特征参数通过 Fisher 准则进行特征降维,构建无人机“声
纹库”,通过灰狼优化算法(GWO)对支持向量机(SVM)中的参数进行优化,建立无人机音频分类模型。 实验结果表明,新特征
参数可弥补单一特征在整个声音频域分辨率低的缺陷,GWO-SVM 音频分类模型可实现在 50 m 距离内无人机探测,识别率达
到 92. 9%,较传统检测方法有显著优势。 |
| 英文摘要: |
| In order to improve the recognition rate of the detection drone, the flight sound of the drone is analyzed in the time and
frequency domain. For the sound characteristics of the drone, so as to better characterize the UAV sound signal, the Mel cepstrum
coefficient (MFCC) and the inverted Mel cepstrum coefficient (IMFCC) are combined. The new feature parameters are used for feature
dimensionality reduction based on Fisher criterion, construct the UAV “voiceprint library”, optimize the parameters in the support vector
machine (SVM) through the gray wolf optimization algorithm (GWO), and establish the UAV audio Classification model. Experimental
results show that the new feature parameters can make up for the low resolution of a single feature in the entire sound and audio domain.
The GWO-SVM audio classification model can achieve drone detection within a distance of 50m, which has significant advantages over
traditional detection methods. |
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