Nianbin Wang1, Ming He1,2, Jianguo Sun1,*, Hongbin Wang1, Lianke Zhou1, Ci Chu1, Lei Chen3
CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 169-181, 2019, DOI:10.32604/cmc.2019.03709
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. More >
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