利用声波和地震波识别军事车辆类型
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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.
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.
引文
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[2]Altmann J,Linev S,Weiss A.Acoustic-seismic detection and classification of military vehicles-developing tools for disarmamentand peace-keeping[J].Applied Acoustics,2002,63:1085-1107.
[3]Duarte M F,Hu H Y.Vehicle classification in distributed sensor networks[J].Journal of Parallel and Distributed Computing,2004,64(7):826-833.
[4]Wu H D,Siegel M,Khosla P.Vehicle sound signature recognition by frequency vector principal component analysis[J].IEEETransactions on Instrumentation and Measurement,1999,48(5):1005-1009.
[5]Liu L.Ground vehicle acoustic signal processing based on biological hearing models[D].University of Maryland,College Park,1999.
[6]Wohler C,Anlauf J K.Real-time object recognition on image sequences with the adaptable time delay neural network algorithm-applications for autonomous vehicles[J].Image and Vision Computing,2001,19(9-10):593-618.
[7]Vapnik V.The Nature of Statistical Learning Theory[M].New York:Springer,1995.
[8]Cao L J,Tay F E H.Support Vector Machine with adaptive parameters in financial time series forecasting[J].IEEE Transactionson Neural Networks,2003,14(6):1506-1518.
[9]Cai C Z,Han L Y,Ji Z L,et al.SVM-Prot:Web-based support vector machine software for functional classification of a proteinfrom its primary sequence[J].Nucleic Acids Research,2003,31(13):3692-3697.
[10]Cai C Z,Han L Y,Ji Z L,Chen Y Z.Enzyme family classification by support vector machines[J].Proteins,2004,55(1):66-76.
[11]Gavrishchaka V V,Ganguli S B.Support vector machine as an efficient tool for high-dimensional data processing:Application tosubstorm forecasting[J].Journal of Geophysical Research-Space Physics,2001,106(A12):29911-29914.
[12]Cai C Z,Wang W L,Chen Y Z.Support vector machine classification of physical and biological datasets[J].International Journalof Modern Physics C,2003,14(5):575-585.
[13]Cai C Z,Wang W L,Sun L Z,Chen Y Z.Protein function prediction via support vector machine approach[J].MathematicalBiosciences,2003,185(2):111-122.
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