Underwater target material classification based on feature extraction of finite element simulated signal

Feature extraction is crucial for underwater target classification and recognition, and this paper aims to propose a scheme for accurately classifying the material features of underwater targets. Finite element simulation software COMSOL is used to obtain echo signals, and then Auto Regressive (AR) coefficient, cepstrum feature, spectral peak and its frequency of Smoothing Pseudo Wigner-Ville Distribution (SPWVD) of echo signals are extracted as the input of the pattern recognition network. The classification results show that the proposed scheme is able to effectively differentiate among four types of materials: metal, brick, plastic and rubber. The scheme also shows good robustness for the target size and shape, and the proposed scheme has the potential to be applied to the practical underwater target classification.