@inproceedings{d8a1bf21c7e245b1886f03ce55985b32,
title = "Underwater Acoustic Target Feature Fusion Recognition Using Multitask Learning",
abstract = "This paper introduces a feature fusion recognition method for underwater targets based on multitask learning (MTL). Initially, to address the limitations of single-feature input in radiated noise spectrum recognition algorithms, this paper proposes an improved Vision Transformer (ViT) for depth learning underwater target classification. Building upon this, the problem of multifeature fusion recognition is reformulated into an MTL framework, and an optimized feature fusion recognition model is proposed using the multiple gradient descent algorithm (MGDA). The performance advantages of the proposed algorithm is validated on the underwater open source dataset DeepShip, offering a novel approach to underwater target feature fusion recognition.",
keywords = "feature fusion, multitask learning, target recognition, underwater acoustic",
author = "Wenfeng Dong and Jin Fu and Xin Wang and Zheng Shen",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 OES China Ocean Acoustics, COA 2024 ; Conference date: 29-05-2024 Through 31-05-2024",
year = "2024",
doi = "10.1109/COA58979.2024.10723455",
language = "英语",
series = "2024 OES China Ocean Acoustics, COA 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 OES China Ocean Acoustics, COA 2024",
address = "美国",
}