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Article

TF-REF-RNN: Time-Frequency and Reference Signal Feature Fusion Recurrent Neural Network for Underwater Backscatter Signal Separation

by
Jun Liu
*,
Shenghua Gong
,
Tong Zhang
,
Zhenxiang Zhao
,
Hao Dong
and
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 / Revised: 24 September 2024 / Accepted: 27 September 2024 / 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.
Keywords: underwater backscatter; signal separation; deep learning; hydroacoustic signals; recurrent neural networks underwater backscatter; signal separation; deep learning; hydroacoustic signals; recurrent neural networks

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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