Feature-Efficient LSTM for Underwater Target Intention Recognition

Yifan Liu, Yi Hao, Weisheng Yan, Xinxin Guo, Yongkang Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Due to the unique characteristics of underwater environments, intention recognition of underwater targets requires high-performance algorithms. A LSTM network incorporating the Adaptive Attention Mechanism is designed in the paper to integrate features and enhance network performance. Furthermore, Meta-action combination feature with comprehensive spatial information consideration is developed, rendering the model more suitable for intention recognition in underwater environments. The ablation experiment confirms that the Enhanced LSTM network improves the focusing ability and adaptability of the model, comprehensively considers the spatial distribution of underwater scenes, and improves the accuracy and robustness of intention recognition. Additionally, comparative analyses with SVM demonstrate that the network developed in this paper is adeptly suited for the time-series-based recognition of underwater target intentions.

源语言英语
主期刊名Proceedings of the 43rd Chinese Control Conference, CCC 2024
编辑Jing Na, Jian Sun
出版商IEEE Computer Society
8649-8654
页数6
ISBN(电子版)9789887581581
DOI
出版状态已出版 - 2024
活动43rd Chinese Control Conference, CCC 2024 - Kunming, 中国
期限: 28 7月 202431 7月 2024

出版系列

姓名Chinese Control Conference, CCC
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议43rd Chinese Control Conference, CCC 2024
国家/地区中国
Kunming
时期28/07/2431/07/24

指纹

探究 'Feature-Efficient LSTM for Underwater Target Intention Recognition' 的科研主题。它们共同构成独一无二的指纹。

引用此