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Multihydrophone Fusion Network for Modulation Recognition.

Haiwang Wang¹, Bin Wang¹, Lulu Wu¹

¹School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This summary is machine-generated.

This study introduces a novel deep learning network for underwater acoustic communication, improving signal modulation recognition using multiple hydrophones. The multihydrophone fusion network (MHFNet) enhances accuracy by 16% compared to existing methods.

Keywords:

fusion network modulation recognition multihydrophone underwater acoustic communication signal

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Area of Science:

Underwater acoustics
Signal processing
Machine learning

Background:

Deep learning (DL) based modulation recognition for underwater acoustic communication signals is typically limited to single hydrophone scenarios.
Multisensory reception offers potential for enhanced performance but requires effective data fusion techniques.

Purpose of the Study:

To propose a novel end-to-end multihydrophone fusion network (MHFNet) for improved modulation recognition in multisensory underwater acoustic communication scenarios.
To leverage neural networks for effective fusion of signals received by multiple hydrophones.

Main Methods:

Developed an end-to-end multihydrophone fusion network (MHFNet) comprising a feature extraction module and a fusion module.
The feature extraction module processes signals from multiple hydrophones.
The fusion module utilizes a neural network to fuse and classify these extracted features.

Main Results:

MHFNet effectively fuses signal features from multiple hydrophones using neural networks.
Experimental results demonstrate MHFNet's superiority over other fusion methods.
Achieved an approximate 16% improvement in classification accuracy on both simulated and practical data.

Conclusions:

The proposed MHFNet significantly enhances modulation recognition performance in multisensory underwater acoustic communication.
MHFNet offers a robust solution for leveraging multihydrophone data for improved signal analysis.
The findings highlight the potential of deep learning and multihydrophone fusion for advancing underwater communication technologies.