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Feature Extraction of Underwater Signals Based on Bispectrum Estimation

Processing underwater acoustic signals for monitoring and classification are difficult problems that have recently attracted attention in the field of underwater signal processing. For these purposes, it is necessary to use a method which could be able to extract the useful information about the processed data. In this paper, an algorithm of extracting feature from radiated noise of underwater targets based on bispectrum estimation is presented. Features were extracted after bispectrum estimation on three target signals and low-dimension feature vectors were obtained. The extracted features were passed into the radial basis function (RBF) neural network classifier. The results show that the bispectrum can restrain the Gaussian noise, at the same time it can obtain the non-Gaussian feature of signal and also reduce the number of dimensions of the feature space. The performance shows that it is properly efficient.