College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China. College of Computer and Information Engineering, Heilongjiang University of Science and Technology Harbin,150022, China. College of Engineering and Computing, Georgia Southern University, Georgia, 30458, USA.
Abstract:
Underwater target recognition is a key technology for underwater acoustic countermeasure. How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals. In this paper, the deep learning model is applied to underwater target recognition. Improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed, based on PNCC applied to underwater noises. Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity. The method is combined with a convolutional neural network in order to recognize the underwater target. Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are well-suited to underwater target recognition using a convolutional neural network. Compared with the combination of convolutional neural network with single acoustic feature, such as MFCC (Mel-scale Frequency Cepstral Coefficients) or LPCC (Linear Prediction Cepstral Coefficients), the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.
