Classification of Ship Radiated Noise Based on Bi-Logarithmic Scale Spectrum and Convolutional Network

XU Yuanchao; CAI Zhiming; KONG Xiaopeng

doi:10.11999/JEIT211407

Volume 44 Issue 6

Jun. 2022

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XU Yuanchao, CAI Zhiming, KONG Xiaopeng. Classification of Ship Radiated Noise Based on Bi-Logarithmic Scale Spectrum and Convolutional Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1947-1955. doi: 10.11999/JEIT211407

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XU Yuanchao, CAI Zhiming, KONG Xiaopeng. Classification of Ship Radiated Noise Based on Bi-Logarithmic Scale Spectrum and Convolutional Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1947-1955. doi: 10.11999/JEIT211407

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Classification of Ship Radiated Noise Based on Bi-Logarithmic Scale Spectrum and Convolutional Network

doi: 10.11999/JEIT211407

College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Received Date: 2021-12-01
Accepted Date: 2022-04-02
Rev Recd Date: 2022-03-31

Available Online: 2022-04-12

Publish Date: 2022-06-21

Abstract

Abstract

The translation equivariance of convolutional layers are not compatible with the linear spectrum. Therefore, the convolutional networks can not carry the long-distance dependency of high-dimensional features. One bi-logarithmic spectrum feature is presented by this paper for classification of ship radiated noise. This bi-logarithmic spectrum rearranges the frequency points of the logarithmic spectrum to ensure the resolution of high frequencies, therefore the substantial deep convolutional network is not necessary. Considering on the prior knowledge that each row of the bi-logarithmic spectrum corresponding to the same one target, a convolutional network as well as an objective function are constructed. Then this network is trained and tested with DeepShip dataset to classify four types of marine vessels, and the results show that, with the same feature dimensions, the classification accuracy of the algorithm proposed by this paper is improved by 2.4% more than the convolutional network with the input feature of linear scale spectrum.

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References(16)

References

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