Target detection and classification using seismic signal processing in unattended ground sensor systems

The most challenging problem in target detection and classification is the extraction of a robust feature vector which can effectively represent a specific type of target. The use of the seismic signals in unattended ground sensor systems brings new challenges to the problem because of the complexity of the seismic waves and their highly dependency on the underlying geology. This paper proposes a new feature extraction algorithm - spectral statistics and wavelet coefficients characterization (SSWCC). SSWCC extracts a feature vector from both the frequency and the time-frequency domain analysis of the seismic signals, including the spectrum, the power spectral density (PSD) and the wavelet coefficients. The SSWCC algorithm is designed for real-time applications, and has shown its robustness and effectiveness through a series of experiments. Extensive performance evaluation is conducted to derive the optimal configuration of the different parameters. The overall classification accuracy can reach as high as 90.