Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen Xinqiang; Ling Jun; Yang YongshengZheng HailinXiong PengwenPostolache OctavianXiong Yong

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Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

机译：Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

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Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第29期|7191296.1-7191296.9|共9页
作者
Chen Xinqiang; Ling Jun; Yang YongshengZheng HailinXiong PengwenPostolache OctavianXiong Yong;
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Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China|Fudan Univ, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China|Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China;

Shanghai Maritime Univ, Inst Logist Sci & Engn, Shanghai 201306, Peoples R China;

Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R ChinaISCTE Inst Univ Lisboa, P-1649026 Lisbon, Portugal|Inst Telecomunicacoes, P-1649026 Lisbon, PortugalHunan Lianzhi Technol Co Ltd, Changsha 410217, Peoples R ChinaNanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China;

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Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

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