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
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| Source Publication | OCEAN ENGINEERING
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| ISSN | 0029-8018 |
| 2022-12-15 | |
| Volume | 266Pages:13 |
| Corresponding Author | Yang, Fan(yangfan@hebut.edu.cn) |
| Abstract | 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. |
| Keyword | Automatic identification system (AIS) Radar track association Graph matching Graph neural network Optimal transport |
| DOI | 10.1016/j.oceaneng.2022.112208 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and De-velopment Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China ; [2019YFB131202] ; [F2019202364] |
| Funding Organization | National Key Research and De-velopment Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China |
| WOS Research Area | Engineering ; Oceanography |
| WOS Subject | Engineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography |
| WOS ID | WOS:000875764900001 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50500 |
| Collection | 融合创新中心 |
| Corresponding Author | Yang, Fan |
| Affiliation | 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 |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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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