SDFA:基于多特征融合的船舶轨迹聚类方法研究

doi:10.11896/jsjkx.211100253

Abstract

Abstract: With the rapid development of ocean transportation,the technology of vessel trajectory mining and analysis has become more and more important.Trajectory clustering has many practical applications in the ship field,such as anomaly detection,position prediction,ship collision avoidance and so on.Traditional trajectory similarity calculation methods are relatively low in accuracy and efficiency,and most existing deep learning methods only extract features of static ones,ignoring the multi-feature combination of dynamic and static features.In order to solve the problem,a static-dynamic-feature fusion model based on convolutional auto-encoder is proposed,which can extract more perfect trajectory features.It makes up for the deficiency of multi-feature fusion technique in vessel trajectory clustering.Experiments on real datasets have demonstrated that compared with traditional methods such as LCSS,DTW and multi-feature extraction model based on deep learning,the proposed model has at least 5%~10% improvement in metrics such as precision,accuracy and so on.

Key words: Convolutional auto-encoder (CAE), Multi-feature fusion, Ship automatic identification system (AIS), Trajectory clustering

CLC Number:

TP391

YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion[J].Computer Science, 2022, 49(6A): 256-260.

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