Abstract
Underwater target recognition is a key technology for underwater acoustic countermeasure. And 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 in the underwater target recognition and the improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed based on PNCC oriented to underwater target noise features. In this coefficient, multitaper and normalized Gammatone filter group are used to improve the anti-noise capacity of PNCC in underwater target recognition, and it is combined with the convolutional neural network to recognize the underwater target. The experiment results show that the acoustic feature presented by ia-PNCC is of higher anti-noise capacity and more adaptive to the underwater target recognition model of 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 PLP) and so on, the combination of ia-PNCC with convolutional neural network improves the underwater target recognition accuracy greatly.
Highlights
With the development of marine resources and the implications of national security, underwater target recognition technology is becoming more widely used
We showed that the underwater target noise vector representation based on the Improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) was able to improve underwater target recognition compared with previous methods, including the MFCC and LPCC
A convolutional neural network was used as the underwater target recognition classifier, with the nonlinear features of the convolutional neural network being used to represent the sonar’s perception capacity
Summary
With the development of marine resources and the implications of national security, underwater target recognition technology is becoming more widely used. It is a key area in the study of target recognition technology and is a vital issue in the field of acoustic signal processing. Scholars at across the globe have studied the topic from many aspects proposed solutions that analyze and resolve problems of underwater target recognition from different perspectives. Based on current requirements, the main challenge in underwater target recognition is one-sidedness of feature representation, resulting from multiple feature representation methods. The combination of time and frequency signals of underwater target noise, and the formation of a feature extraction method based on both is the main focus of this paper
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