复杂环境下的窄口段水域AIS轨迹重塑方法研究  

Research on ship AIS trajectory reconstruction for narrow-mouth section waters under complex environment

作　　者：白响恩[1] 方明权 徐笑锋 肖英杰[1] 吴永明 陈诺 BAI Xiang'en;FANG Mingquan;XU Xiaofeng;XIAO Yingjie;WU Yongming;CHEN Nuo(Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China;Ningbo Pilot Station,Ningbo 315000,China)

机构地区：[1]上海海事大学商船学院,上海201306 [2]宁波引航站,浙江宁波315000

出　　处：《中国航海》2024年第3期106-113,共8页Navigation of China

基　　金：国家自然科学基金(42176217);长三角关键水域通航船舶态势智能监测技术(Z20228005)。

摘　　要：针对窄口段航道船舶自动识别系统(AIS)轨迹信息易出错和缺失的问题,对宁波舟山港条帚门水域及其船舶基本情况进行统计分析,并提出AIS船舶轨迹修复方法。对异常数据归一化处理构建预输入模型,提出一种基于DS(Dempster-Shafer)证据融合理论的双向长短期记忆的多层前馈网络修复方法,将此方法与BP(Back Propagation)、长短期记忆网络(LSTM)方法进行试验论证比较。结果表明:所提方法在轨迹修复4个维度上均优于其他方法,且连续丢失点在20个以内,机器学习平均损失率为0.048 3%,均低于其他方法。修复后的船舶轨迹信息更加完整也更符合船舶运动规律。Aiming at the problem that the track information of AIS(Automatic Identification System)of ships in the narrow-mouth section of waterway is prone to error and missing,this paper conducts a statistical analysis of the actual waters and the basic situation of ships in the narrow-mouth section of Ningbo Strip Broom Gate and proposes a method to reconstruct AIS ship trajectories.The proposed method constructs a pre-input model by normalizing the anomalous data and uses a multi-layer feedforward network restoration method based on Dempster-Shafer evidence theory with bi-directional long and short-term memory.The method is compared with other machine learning methods such as BP(Back Propagation)and LSTM(Long Short Term Memory)through experimental demonstration,and the results show that it outperforms other methods in all four dimensions of trajectory restoration.Moreover,the average loss rate of machine learning is 0.0483%within 20 consecutive lost points,which is lower than other methods.The repaired ship trajectory and data are more complete and consistent with the ship motion pattern.

关 键 词：窄口段水域 船舶自动识别系统数据 Dempster-Shafer证据融合理论 轨迹重塑 

分 类 号：U675.7[交通运输工程—船舶及航道工程]