Recognition of military vehicles by using acoustic and seismic signals

Abstract

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.