利用声波和地震波识别军事车辆类型  

Recognition of Military Vehicles by Using Acoustic and Seismic Signals

作　　者：肖汉光[1] 蔡从中[1] 廖克俊[1] 

机构地区：[1]重庆大学应用物理系

出　　处：《系统工程理论与实践》2006年第4期108-113,共6页Systems Engineering-Theory & Practice

基　　金：重庆大学与新加坡国立大学国际合作研究项目

摘　　要：声波和地震波是军事车辆类型识别的重要信息源,针对军事车辆运动时产生的声波和地震波,采用短时傅里叶变换提取其波形数据的频谱特征向量,提出基于能量频谱密度进行二次特征选择,构造声波和地震波频谱特征向量子空间,从而降低了特征向量的维数.应用支持向量机(SVM)和最近邻分类法(KNN)分别对声波和地震波数据来进行军事车辆分类,结果表明:基于能量频谱密度的二次特征选择方法能有效地构造出声波和地震波的特征子空间,由此得到的分类准确率高于传统的特征选择方法.通过比较SVM和KNN的分类结果可以得出SVM的分类效果优于KNN.Acoustic and seismic wave data play an important role in the recognition of military vehicles. We utilized the Short Time Fourier Transform （STFT） approach to extract the spectral feature vectors from the acoustic and seismic wave data of military vehicles. The power spectral density （PSD）-based feature selection method was proposed to reconstruct the feature subspace of the acoustic and seismic spectral vectors for decreasing the dimension of the feature vectors. Two type military vehicles were respectively classified by using the acoustic and seismic wave data. The classification results by Support Vector Machine （SVM） and K-Nearest Neighbor （KNN） revealed that the PSD-based method of feature selection could represent the seismic and acoustic signals more efficiently in feature subspace and the accuracy is better than the traditional feature selection method which is obtained via directly feature-range cutting off. It also could be concluded that the effect of SVM to recognize the military vehicles is superior to that of KNN.

关 键 词：特征提取 车辆识别 能谱密度 傅里叶变换 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O174.22[自动化与计算机技术—控制科学与工程]