Underwater target identification method based on semi-tensor product neural network

Abstract

The invention provides an underwater target identification method based on a semi-tensor product neural network, and the method comprises the steps: receiving an underwater sound signal through an underwater sonar sensor, and enabling the time domain and frequency domain information of the sound signal to be presented in a LOFAR map through short-time Fourier transform; constructing a data sample semi-tensor product neural network by taking a LOFAR map sample as an input characteristic matrix; dividing the received underwater acoustic signals into a training set and a verification set, and inputting the training set and the verification set into a semi-tensor product neural network for training and verification; selecting different hyper-parameters, carrying out model training on a semi-tensor product neural network by using a training set, comparing test effects of a verification set, and determining hyper-parameters with high test accuracy; and finally inputting the currently acquired sound signal of the underwater target into the trained semi-tensor product neural network after model, and giving a judgment result. The underwater target recognition rate can be improved, the application scene is expanded, and the method is suitable for recognizing the underwater target in complex marine environment noise.

Description

technical field [0001] The invention belongs to the field of signal processing and relates to methods such as neural network, acoustic signal processing, half-tensor product multiplication and underwater target recognition. Background technique [0002] Target classification and recognition is of great significance to various research fields. The traditional target classification and recognition is to artificially extract various features, and then construct a classifier for classification and recognition. With the rapid development of computer vision technology, the target classification and recognition technology based on deep learning has been widely studied, and it has also achieved a classification and recognition effect that surpasses that of humans. However, at present, it mainly conducts research in the fields of computer vision such as images and videos, and the research on acoustic signals also focuses on speech signal processing and natural language processing, an...

AI Technical Summary

Problems solved by technology

Traditional methods are greatly affected by artificial feature extraction and environmental noise. These recognition models are only a symbolic system, which reduces the ability of modeling. The recognition performance of acoustic signals in complex environments will be greatly reduced, resulting in complex The classification recognition rate is not ideal in the changing marine environment noise

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