TY - GEN
T1 - Feature-Efficient LSTM for Underwater Target Intention Recognition
AU - Liu, Yifan
AU - Hao, Yi
AU - Yan, Weisheng
AU - Guo, Xinxin
AU - Wang, Yongkang
N1 - Publisher Copyright:
© 2024 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - deep learning
KW - intention recognition
KW - Long Short-Term Memory network
KW - underwater target intention recognition
UR - http://www.scopus.com/inward/record.url?scp=85205494757&partnerID=8YFLogxK
U2 - 10.23919/CCC63176.2024.10662460
DO - 10.23919/CCC63176.2024.10662460
M3 - 会议稿件
AN - SCOPUS:85205494757
T3 - Chinese Control Conference, CCC
SP - 8649
EP - 8654
BT - Proceedings of the 43rd Chinese Control Conference, CCC 2024
A2 - Na, Jing
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 43rd Chinese Control Conference, CCC 2024
Y2 - 28 July 2024 through 31 July 2024
ER -