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Multi-target association algorithm of AIS-radar tracks using graph matching-based deep neural network
Yang, Yipu1,2; Yang, Fan1; Sun, Liguo2; Xiang, Ti2,3; Lv, Pin2
发表期刊OCEAN ENGINEERING
ISSN0029-8018
2022-12-15
卷号266页码:13
通讯作者Yang, Fan(yangfan@hebut.edu.cn)
摘要Automatic Identification System(AIS) and radar track association is a challenging subject in dense scenes in which there are some undesirable factors, such as multiple targets, complicated target movement patterns, and asynchronous track information, causing inaccurate and inefficient track correlation. Therefore, this research focuses on the optimization problem of AIS and radar track association in dense scenes. Time-series data of tracks are transformed into the distribution features in a graph, which is free from the close dependence of the traditional algorithm on the pre-processing of the time alignment. To this end, an end-to-end deep network pipeline based on graph matching is proposed to overcome the influence of the above factors. It involves a multiscale point-level feature extractor to embed local features. Meanwhile, we devise a cluster-level graph neural network(GNN) with self-cross attention, which can look for global cues that help us disambiguate the correct correlation from complex tracks. Graph matching is estimated by tackling a differentiable optimal transport problem, which minimizes the transport cost and then achieves global optimal track association. Experiments show that the proposed method outperforms other approaches and achieves an ideal score(the precision rate and the recall rate are 0.941 and 0.91, respectively) in our built dataset.
关键词Automatic identification system (AIS) Radar track association Graph matching Graph neural network Optimal transport
DOI10.1016/j.oceaneng.2022.112208
收录类别SCI
语种英语
资助项目National Key Research and De-velopment Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China ; [2019YFB131202] ; [F2019202364]
项目资助者National Key Research and De-velopment Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China
WOS研究方向Engineering ; Oceanography
WOS类目Engineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography
WOS记录号WOS:000875764900001
出版者PERGAMON-ELSEVIER SCIENCE LTD
引用统计
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50500
专题融合创新中心
通讯作者Yang, Fan
作者单位1.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300400, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100089, Peoples R China
第一作者单位中国科学院自动化研究所
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GB/T 7714
Yang, Yipu,Yang, Fan,Sun, Liguo,et al. Multi-target association algorithm of AIS-radar tracks using graph matching-based deep neural network[J]. OCEAN ENGINEERING,2022,266:13.
APA Yang, Yipu,Yang, Fan,Sun, Liguo,Xiang, Ti,&Lv, Pin.(2022).Multi-target association algorithm of AIS-radar tracks using graph matching-based deep neural network.OCEAN ENGINEERING,266,13.
MLA Yang, Yipu,et al."Multi-target association algorithm of AIS-radar tracks using graph matching-based deep neural network".OCEAN ENGINEERING 266(2022):13.
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