Underwater acoustic signal target classification and recognition method based on deep learning

Abstract

The invention belongs to the technical field of underwater acoustic signal processing, and particularly relates to an underwater acoustic signal target classification and recognition method based on deep learning. The method comprises the following steps: (1) carrying out feature extraction on an original underwater acoustic signal through a Gammatone filtering cepstrum coefficient (GFCC) algorithm; (2) extracting instantaneous energy and instantaneous frequency by utilizing an improved empirical mode decomposition (MEMD) algorithm, fusing the instantaneous energy and the instantaneous frequency with characteristic values extracted by a GFCC algorithm, and constructing a characteristic matrix; (3) establishing a Gaussian mixture model GMM, and keeping the individual characteristics of theunderwater acoustic signal target; And (4) finishing underwater target classification and recognition by using a deep neural network (DNN). According to the underwater acoustic signal target classification and recognition method, the problems that a traditional underwater acoustic signal target classification and recognition method is single in feature extraction and poor in noise resistance can be solved, the underwater acoustic signal target classification and recognition accuracy can be effectively improved, and certain adaptability is still achieved under the conditions of weak target acoustic signals, long distance and the like.

Description

technical field [0001] The invention belongs to the technical field of underwater acoustic signal processing, and in particular relates to an underwater acoustic signal target classification and recognition method based on deep learning. Background technique [0002] The development and utilization of marine resources is an important way to achieve sustainable development. Underwater target classification and recognition technology can help to better conduct marine biological surveys and marine exploration. The ocean environment is complex and changeable, electromagnetic waves are greatly attenuated in water, and most underwater targets have their own specific acoustic characteristics, so it is more appropriate to choose the signal form of sound waves to study underwater targets. In the military field, underwater acoustic signal target classification and recognition technology can detect underwater targets accurately, timely, and covertly, and provide accurate information fo...

AI Technical Summary

Problems solved by technology

In the classification and recognition of underwater targets, classification and recognition methods such as neural network, hidden Markov model and support vector machine (SVM) are usually used. These traditional underwater acoustic signal target classification and recognition methods use shallow structure algorithms to complete the classification and recognition of targets. Lack of the ability to learn the essential characteristics of the data set from a large number of data samples. At present, the types of underwater acoustic signal targets are complex, coupled with the complex environment of the seabed and a lot of noise, which makes the traditional classification and recognition methods unable to accurately complete the underwater acoustic signal. Target Classification and Recognition

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