基于自编码器的飞机类型识别方法  

Aircraft Type Recognition Method Based on Auto-encoder

作　　者：张朝柱[1] 黄妤宁 ZHANG Chaozhu;HUANG Yuning(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院

出　　处：《无线电工程》2019年第7期601-605,共5页Radio Engineering

基　　金：中央高校基本科研业务费自由探索计划项目

摘　　要：针对人工监听识别飞机类型难度大的问题,提出了根据不同飞机发动机产生的不同噪声,通过特征提取,进而分类识别出飞机类型的一种方法。在梅尔倒谱系数(MFCC)算法特征提取的基础上,对提取的24维特征向量通过自编码器进行分类,对分类的准确率进行了仿真。实验结果表明,每一类声信号准确率均高于85%,且平均识别准确率为95.98%。针对单类别实际飞机声信号的分类准确率较其他类别准确率差的问题,提出了通过小波包分解-MFCC联合特征提取对自编码器进行优化。实验结果表明,每一类声信号准确率均高于90%,且平均准确率为97.74%。Aiming at the difficulty of identifying aircraft types by artificial monitoring,a method of classifying and identifying aircraft types by extracting features according to the different noise produced by different aircraft engines is proposed.On the basis of feature extraction with MFCC algorithm,24 extracted feature vectors are classified by auto-encoder,and the classification accuracy is simulated.The experimental results show that the accuracy of each kind of acoustic signal is higher than 85%,and the average recognition accuracy was 95.98%.To solve the problem that the classification accuracy of single category of actual aircraft acoustic signals is lower than that of other categories,the auto-encoder is optimized through wavelet packet decomposition-MFCC combined feature extraction.The experimental results show that the accuracy of each kind of acoustic signals is higher than 90% and the average accuracy is 97.74%.

关 键 词：飞机类型识别 梅尔倒谱系数 联合特征提取 机器学习 自编码器 

分 类 号：TN912.3[电子电信—通信与信息系统]