Identification of the Shaft-Rate Electromagnetic Field Induced by a Moving Ship Using Improved Learning-Based and Spectral-Direction Methods

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成果类型：

期刊论文

作者：

Hu, Shuanggui;Zhang, Liang;Tang, Jingtian;Li, Guang;Yang, Haiyan;...

通讯作者：

Zhang, L;Tang, JT

作者机构：

[Xiang, Jingnian; Yang, Haiyan; Hu, Shuanggui] China Univ Min & Technol, Sch Resources & Geosci, Xuzhou 221008, Peoples R China.

[Zhang, Liang; Zhang, L] Guizhou Univ, Key Lab Adv Mfg Technol, Minist Educ, Guiyang 550025, Peoples R China.

[Tang, Jingtian] Cent South Univ, Sch Geosci & Infophys, Changsha 410083, Peoples R China.

[Li, Guang] East China Univ Technol, Nanchang Key Lab Intelligent Sensing Technol & Ins, Nanchang 330013, Peoples R China.

[Xu, Zhenhuan] Taiyuan Univ Technol, Coll Comp Sci & Technol, Taiyuan 030600, Peoples R China.

通讯机构：

[Zhang, L ] G

[Tang, JT ] C

Guizhou Univ, Key Lab Adv Mfg Technol, Minist Educ, Guiyang 550025, Peoples R China.

Cent South Univ, Sch Geosci & Infophys, Changsha 410083, Peoples R China.

语种：

英文

关键词：

Deep residual network (ResNet);shaft-rate electromagnetic field;signal identification;spectral-direction analysis;variational modal decomposition;Deep residual network (ResNet);shaft-rate electromagnetic field;signal identification;spectral-direction analysis;variational modal decomposition

期刊：

IEEE Transactions on Geoscience and Remote Sensing

ISSN：

0196-2892

年：

2024

卷：

页码：

1-11

DOI：

10.1109/TGRS.2024.3436031

基金类别：

National Key Research and Development Program of China (Grant Number: 2023YFC3008901 and 2023YFF0718000) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 42230811) 10.13039/501100004761-Natural Science Foundation of Hainan Province (Grant Number: 2021JJ40024) Guizhou University Basic Research Project (Grant Number: [2023]44) Xuzhou Key Research and Development Plan (Grant Number: KC20052) Basic Applied Research and Soft Science Research Plan of Yiyang (Yi Caijiao Zhi [2022]108)

机构署名：

本校为其他机构

院系归属：

信息与电子工程学院

摘要：

The use of the shaft-rate electromagnetic fields generated by moving ships for detection and sensing purposes has several advantages, including effective target recognition and excellent concealment. It offers a solution to the challenges faced in detecting underwater targets. In this study, we propose a method to identify and analyze the shaft-rate electromagnetic field signals using an improved deep learning algorithm and a spectral-direction analysis technique. Initially, we apply variational mode decomposition (VMD) to identify the multifrequency characteristics of both synthesized and rea...

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Identification of the Shaft-Rate Electromagnetic Field Induced by a Moving Ship Using Improved Learning-Based and Spectral-Direction Methods

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