Underwater target identification method based on convolutional neural network

Abstract

The invention discloses an underwater target identification method based on a convolutional neural network, comprising the following steps: firstly, simulating radiation noise of an underwater sound target; secondly, acquiring underwater target tracking beams; thirdly, acquiring a time-frequency graph of each target tracking beam, wherein all the time-frequency graphs are segmented according to fixed duration and divided into training samples and test samples; fourthly, performing data enhancement, size magnification and tailoring on the samples; fifthly, inputting the training samples provided with a label into a built convolutional neural network, performing supervised learning, and obtaining each layer parameter of the convolutional neural network; sixthly, initializing the network by utilizing each layer parameter, and obtaining the convolutional neural network with an underwater target identification function; and seventhly, acquiring radiation noise of a to-be-tested navigation target by a towed array, converting into a time-frequency graph and segmenting, inputting the segmented subgraphs into the convolutional neural network as to-be-tested samples, obtaining an identification result of each subgraph, and taking an identified target with the highest target quantity during identification as a final identification result. The method disclosed by the invention can enable underwater identification to maintain relatively high accuracy and speed under high ocean background noise condition.

Description

technical field [0001] The invention belongs to the field of underwater target feature extraction and target recognition, and in particular relates to a convolutional neural network-based underwater target recognition method. Background technique [0002] Hydrophone towed linear array sonar, referred to as towed array, is an acoustic detection system towed at a certain distance at the stern of the ship. By receiving the radiation noise of the navigation target itself, it extracts the characteristics of the radiation noise, thereby detecting the presence or absence of the target and estimating parameters of the target. It has the characteristics of strong detection ability, low detection frequency, strong hydrological adaptability and no blind spots. [0003] Underwater target recognition is a key technology to realize the intelligence of underwater acoustic equipment and weapon systems. Therefore, the automatic underwater target recognition technology has been highly valued...

AI Technical Summary

Problems solved by technology

[0006] Due to the complexity of underwater acoustic target recognition itself, the current underwater acoustic target recognition can only be an auxiliary decision-making system for sonar, and there is still a long way to go before the real solution of the problem

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