Research on Classification Algorithm of Ship Radiated Noise Data Based on Generative Adversarial Network

LI Li; LI Xiangxin; YIN Jingwei

doi:10.11999/JEIT211077

Volume 44 Issue 6

Jun. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 1974-1983

LI Li, LI Xiangxin, YIN Jingwei. Research on Classification Algorithm of Ship Radiated Noise Data Based on Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1974-1983. doi: 10.11999/JEIT211077

Citation:

LI Li, LI Xiangxin, YIN Jingwei. Research on Classification Algorithm of Ship Radiated Noise Data Based on Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1974-1983. doi: 10.11999/JEIT211077

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Research on Classification Algorithm of Ship Radiated Noise Data Based on Generative Adversarial Network

doi: 10.11999/JEIT211077

1.
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Received Date: 2021-10-08
Accepted Date: 2022-05-23
Rev Recd Date: 2022-05-19

Available Online: 2022-05-25

Publish Date: 2022-06-21

Abstract

Abstract

In recent years, ship target recognition based on machine learning has become an important research direction in the field of underwater acoustic signal processing, but the acquisition of underwater acoustic target signal is difficult, and the problem of insufficient sample size and imbalance leads easily to the poor recognition effect of target classification model. A ship noise data classification method based on Generative Admission-Network (GAN) is proposed in this paper. This method uses generative admission-learning theory to generate pseudo-DEMON modulation spectrum data with stronger nonlinear characteristics and richer feature differences compared with traditional data enhancement algorithms to alleviate the problem of insufficient training sample size. Then, the output of the whole connection layer in the traditional generative adversarial network is replaced by an ensemble classifier which is better at solving the problem of small samples, so as to reduce the dependence of the classifier on the amount of data and improve further the performance of the classification model. Finally, experimental results based on real samples show that, compared with traditional data enhancement algorithms and generative adversarial networks, the proposed method can improve the classification performance of models with insufficient samples more effectively.
- Ship target recognition,
- Machine learning,
- Data augmentation,
- Generative Adversarial Network (GAN)

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References(18)

References

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