Work

Underwater Acoustic Target Recognition Based on Sub-Regional Feature Enhancement and Multi-Activated Channel Aggregation

Year: 2024

Type: article

Abstract: Feature selection and fusion in ship radiated noise-based underwater target recognition have remained challenging tasks. This paper proposes a novel feature extraction method based on multi-dimensional feature selection and fusion. Redundant features are filtered through feature visualization techniques. The Sub-regional Feature Enhancement modules (SFE) and Multi-activated Channel Aggregation modules (MCA) within the neural network are utilized to achieve underwater target recognition. Experimental results indicate that our network, named Sub-Regional Channel Aggregation Net (SRCA-Net), utilizing 3-s sound segments for ship radiated noise recognition, surpasses existing models, achieving an accuracy of 78.52% on the public DeepShip dataset.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse12111952

Authors: Zhongxiang Zheng, Peng Liu

Institutions: Chinese Academy of Sciences, Institute of Acoustics

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $2,955