Knowledge Commons of Institute of Automation,CAS
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 |
ISSN | 0029-8018 |
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 |
DOI | 10.1016/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 | |
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 |
推荐引用方式 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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