TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block

doi:10.1016/j.knosys.2023.111202

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统认知与决策实验室

	TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block
	Xiang, Ti 1,2; Lv, Pin 2; Sun, Liguo 2; Yang, Yipu 2,3; Hao, Jiuwu 1,2
刊名	KNOWLEDGE-BASED SYSTEMS
	2024-01-11
卷号	283 页码:13
关键词	Track classification Multi-feature fusion Marine radar Transformer
ISSN号	0950-7051
DOI	10.1016/j.knosys.2023.111202
通讯作者	Lv, Pin()
英文摘要	The shipping industry has experienced rapid growth in recent years, prompting a need for advanced target recognition technology based on marine radar. This paper introduces the Track Classification Model (TCM), a novel approach for classifying track sequences in real scenarios. The TCM utilizes a feature extraction network based on multi-feature fusion, taking radar echo images and motion information of the target as input, to improve classification accuracy. Additionally, the paper also presents a dataset production method that addresses the issue of missing labels, a critical problem in track sequence classification. Through ablation experiments, the paper demonstrates the effectiveness of the design strategy, with the multi-feature fusion network successfully extracting features and achieving superior performance over single feature extraction networks. The results show that increasing the number of input track points and raising the upper limit of the input sequence leads to improved classification accuracy. Finally, in real scenarios, the proposed model outperforms other algorithms, showcasing its high engineering application value.
资助项目	National Key Research and Development Program of China[2022ZD0116409]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:001124036300001
资助机构	National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55024]
专题	复杂系统认知与决策实验室
通讯作者	Lv, Pin
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 300400, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300400, Peoples R China
推荐引用方式 GB/T 7714	Xiang, Ti,Lv, Pin,Sun, Liguo,et al. TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block[J]. KNOWLEDGE-BASED SYSTEMS,2024,283:13.
APA	Xiang, Ti,Lv, Pin,Sun, Liguo,Yang, Yipu,&Hao, Jiuwu.(2024).TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block.KNOWLEDGE-BASED SYSTEMS,283,13.
MLA	Xiang, Ti,et al."TCM Model for improving track sequence classification in real scenarios with Multi-Feature Fusion and Transformer Block".KNOWLEDGE-BASED SYSTEMS 283(2024):13.