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Open AccessArticle
TF-REF-RNN: Time-Frequency and Reference Signal Feature Fusion Recurrent Neural Network for Underwater Backscatter Signal Separation
by
Jun Liu
Jun Liu
Jun Liu is a professor and doctoral supervisor at the School of Electronic Information Engineering, [...]
Jun Liu is a professor and doctoral supervisor at the School of Electronic Information Engineering, Beihang University, is a national-level science and technology leader. He received his doctorate from the University of Connecticut in August 2013. He has presided over and participated in more than 20 key R&D projects, National Natural Science Foundation projects, and published more than 100 papers. He has been authorized with 10 national invention patents and 12 software copyrights. He has insisted on combining industry, academia and research for many years and won the first prize in China's Industry-University-Research Cooperation Innovation Achievement Award (ranked first), the first prize in China General Chamber of Commerce Science and Technology Award (ranked first) and many other awards. He obtained a Bachelor's Degree at Wuhan University in Computer Science and Technology from 1998.9 to 2002.7 and a Master's Degree at the University of Connecticut in Computer Science and Engineering from 2008.9 to 2011.8.
*,
Shenghua Gong
Shenghua Gong ,
Tong Zhang
Tong Zhang ,
Zhenxiang Zhao
Zhenxiang Zhao ,
Hao Dong
Hao Dong and
Jie Tan
Jie Tan
School of Electronic Information Engineering, Beihang University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(19), 3635; https://doi.org/10.3390/rs16193635 (registering DOI)
Submission received: 28 August 2024
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Revised: 24 September 2024
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Accepted: 27 September 2024
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Published: 29 September 2024
Abstract
Underwater wireless sensor networks play an important role in exploring the oceans as part of an integrated space–air–ground–ocean network. Because underwater energy is limited, the equipment’s efficiency is significantly impacted by the battery duration. Underwater backscatter technology does not require batteries and has significant potential in positioning, navigation, communication, and sensing due to its passive characteristics. However, underwater backscatter signals are susceptible to being swamped by the excitation signal. Additionally, the signals from different reflection signals share the same frequency and overlap, and contain fewer useful features, leading to significant challenges in detection. In order to solve the above problems, this paper proposes a recurrent neural network that introduces time-frequency and reference signal features for underwater backscatter signal separation (TF-REF-RNN). In the feature extraction part, we design an encoder that introduces time-frequency domain features to learn more about the frequency details. Additionally, to improve performance, we designed a separator that incorporates the reference signal’s pure global information features. The proposed TF-REF-RNN network model achieves metrics of 28.55 dB SI-SNRi and 19.51 dB SDRi in the dataset that includes shipsEar noise data and underwater simulated backscatter signals, outperforming similar classical methods.
Share and Cite
MDPI and ACS Style
Liu, J.; Gong, S.; Zhang, T.; Zhao, Z.; Dong, H.; Tan, J.
TF-REF-RNN: Time-Frequency and Reference Signal Feature Fusion Recurrent Neural Network for Underwater Backscatter Signal Separation. Remote Sens. 2024, 16, 3635.
https://doi.org/10.3390/rs16193635
AMA Style
Liu J, Gong S, Zhang T, Zhao Z, Dong H, Tan J.
TF-REF-RNN: Time-Frequency and Reference Signal Feature Fusion Recurrent Neural Network for Underwater Backscatter Signal Separation. Remote Sensing. 2024; 16(19):3635.
https://doi.org/10.3390/rs16193635
Chicago/Turabian Style
Liu, Jun, Shenghua Gong, Tong Zhang, Zhenxiang Zhao, Hao Dong, and Jie Tan.
2024. "TF-REF-RNN: Time-Frequency and Reference Signal Feature Fusion Recurrent Neural Network for Underwater Backscatter Signal Separation" Remote Sensing 16, no. 19: 3635.
https://doi.org/10.3390/rs16193635
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