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Identification of the Shaft-Rate Electromagnetic Field Induced by a Moving Ship Using Improved Learning-Based and Spectral-Direction Methods | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

The use of the shaft-rate electromagnetic fields generated by moving ships for detection and sensing purposes has several advantages, including effective target recogniti...Show More

Abstract:

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 real extremely low-frequency (ELF) electromagnetic signals, creating a reliable sample library for deep learning. Next, we develop an improved deep learning model that combines the residual network (ResNet) with the aforementioned sample library to accurately detect the weak narrowband electromagnetic field signals hidden within the noise. Additionally, we use the spectral-direction analysis method to estimate the direction of the ship’s movement. Finally, we validate our proposed method through a synthetic model and a field experiment. The results demonstrate the effectiveness of our approach in identifying the shaft-rate electromagnetic field signals and accurately estimating the direction of moving ships. The developed method shows the potential for accurate sensing and localization of moving ships.
Article Sequence Number: 5922511
Date of Publication: 31 July 2024

ISSN Information:

Funding Agency:

School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China
Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China
School of Geosciences and Info-physics of Central South University, Changsha, China
Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang, China
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
College of Information and Electronic Engineering, Hunan City University, Yiyang, China
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China

I. Introduction

Localization of underwater vehicles or ships is of great importance in both civil and military applications [1], [2], [3], [4]. However, the progress made in the denoising technology for ships has presented significant challenges for acoustic-based methods. As a result, the use of the extremely low-frequency (ELF) electromagnetic field detection technologies has become more prominent. The adoption of these technologies offers several benefits, such as effective concealment and improved target recognition capabilities [5], [6]. Due to the significant attenuation of electromagnetic fields in seawater, the frequency range is limited to the ELF band of 0 to 0.3 kHz. These technologies are used mainly in marine warfare to locate and track moving targets and to activate multiinfluence mines [7]. Generally, the ELF electromagnetic fields include underwater electric potential, corrosion-related magnetic fields, and the shaft-rate field generated by shaft rotation. In this study, we focus on the shaft-rate electromagnetic field, especially the shaft-rate magnetic fields, which has attracted attention from researchers due to its distinct characteristics associated with shaft rotation.

School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China
Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China
School of Geosciences and Info-physics of Central South University, Changsha, China
Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang, China
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
College of Information and Electronic Engineering, Hunan City University, Yiyang, China
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, China

References

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