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

Authors :
Jun Liu
Shenghua Gong
Tong Zhang
Zhenxiang Zhao
Hao Dong
Jie Tan
Source :
Remote Sensing, Vol 16, Iss 19, p 3635 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 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.

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
19
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.bd0faa308a5f470c8390e8c75021120f
Document Type :
article
Full Text :
https://doi.org/10.3390/rs16193635