Computational techniques for classification of military vehicles using seismic signatures

In this research work a seismic classification system is designed to distinguish between tracked and wheeled vehicle classes. Owing to the extreme non-stationary nature of seismic signals, choosing robust features is an important aspect for the purpose of classification. To obtain a varied feature set different signal processing techniques namely Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), Hilbert-Huang Transform (HHT) and Wavelet Transform (WT) are investigated. Dominant features are identified from the feature bank using Principal Component Analysis (PCA). This choice of optimal and robust features leads to a better class discrimination. It is observed that the classification results obtained by the varied feature set followed by optimization has improved classification accuracy of 95% than using features extracted from individual signal processing techniques.