Ship radiation noise identification method, device and equipment and readable storage medium

Abstract

The invention discloses a ship radiation noise identification method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining ship radiation noise data; inputting the ship radiation noise data into a pre-constructed ship radiation noise identification model, wherein the ship radiation noise identification model outputs a ship radiation noise category; wherein the construction of the ship radiation noise identification model is based on a network model of an improved ResNet, the network model of the improved ResNet is based on a ResNet-18 residual network, and an attention SE module is introduced. According to the invention, the identification accuracy of the ship radiation noise is improved.

Description

Technical field [0001] The invention involves the field of water sound signal recognition technology, which specifically involves a ship radiation noise recognition method, device, equipment and readable storage medium. Background technique [0002] Under the current international situation, the war environment is becoming more and more complex. Whether it can obtain marine security information in a timely and accurate manner, identify water sound signals, and provide accurate battlefield conditions for command systems at all levels. Essence Based on traditional machine learning methods to build a water sound signal recognition system, the main steps include feature extraction, feature selection, and classifier design. However, with the continuous increase of data volume, traditional methods are gradually difficult to meet existing identification tasks. [0003] In recent years, with the rise of deep learning technology, deep neural networks, as the most classic deep learning alg...

AI Technical Summary

Problems solved by technology

Building an underwater acoustic signal recognition system based on traditional machine learning methods, the main steps include feature extraction, feature selection and classifier design, but with the increasing amount of data, traditional methods are gradually difficult to meet the existing recognition tasks

[0003] In recent years, with the rise of deep learning technology, deep neural network, as the most classic deep learning algorithm, overcomes the shortcomings of traditional machine learning based on expert knowledge to extract features and train corresponding classifiers, and has the ability to autonomously mine a large number of hidden information in underwater acoustic signals. The ability to integrate feature extraction, selection and classifier judgment has improved the recognition performance to a certain extent, but the accuracy and stability of the recognition model are still relatively poor, resulting in low recognition accuracy of ship radiation noise

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