A comparative study of feature extraction and classification methods for military vehicle type recognition using acoustic and seismic signals

It is a difficult and important task to classify the types of military vehicles using the acoustic and seismic signals generated by military vehicles. For improving the classification accuracy, we investigate different feature extraction methods and 4 classifiers. Short Time Fourier transform (STFT) is employed for feature extraction from the primary acoustic and seismic signals. Independent component analysis (ICA) and principal component analysis (PCA) are used to extract features further for dimension reduction of feature vector. Four different classifiers including decision tree (C4.5), K-nearest neighbor (KNN), probabilistic neural network (PNN) and support vector machine (SVM) are utilized for classification. The classification results indicate the performance of SVM surpasses those of C4.5, KNN, and PNN. The experiments demonstrate ICA and PCA are effective methods for feature dimension reduction. The results showed the classification accuracies of classifiers with PCA were superior to those of classifiers with ICA. From the perspective of signal source, the classification accuracies of classifiers using acoustic signals are averagely higher 15% than those of classifiers using seismic signals.