SDFA:基于多特征融合的船舶轨迹聚类方法研究

SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion

随着航运业的快速发展,船舶轨迹挖掘与分析技术变得愈发重要,轨迹聚类在船舶领域有很多实际应用,如异常检测、位置预测、船舶避碰等。传统的轨迹相似度计算方法在精确度和效率上都较为低下,而现有的基于深度学习的方法大多数只提取静态特征,忽视了静态与动态的多特征的综合提取。为了解决这一问题,提出了一种基于卷积自编码器的静态-动态特征融合模型,用于提取更完善的船舶轨迹特征,弥补了多特征融合技术在船舶轨迹聚类应用方面的不足。在真实数据集上的实验结果表明,相比LCSS,DTW等传统方法以及基于深度学习的多特征提取模型,所提模型在精确率、准确率等指标上均至少有5%~10%的提升。

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.