基于模糊隶属度的船舶中断航迹关联识别方法

doi:10.3969/j.issn.0255-8297.2023.02.010

摘要/Abstract

摘要： 为实现对中断的航迹进行准确的关联识别，形成统一的海上态势，提出了基于航迹预测和模糊隶属度评价的航迹关联识别方法。采用多项式拟合方法进行航迹的双向推测，选取位置、航向、速度作为模糊因素集，采用岭型模糊隶属度函数计算预测的航迹点之间各模糊因素的隶属度并加权得到单时刻隶属度，构造加权函数计算综合隶属度，通过阈值门限判断两航迹是否关联。在仿真实验中，所提方法精确率大于90%，召回率大于80%，关联性能优于传统方法。为进一步研究该方法的场景适应性，仿真了不同场景下的船舶航迹雷达监测数据，包括中断区间内船舶运动稳定场景、速度变化场景、航向变化场景等，船舶运动状态稳定时关联结果的召回率大于90%，中断区间内速度变化小于8 km/h时召回率大于84%，航向变化低于25–时召回率大于88%。该方法为解决非合作船舶交叉环境下的中断航迹关联识别问题提供了有效途径。

关键词: 中断航迹, 航迹关联, 航迹预测, 岭型模糊隶属度

Abstract: In order to integrate the vessels’ tracks from different monitoring sources and form a unified maritime posture, a track correlation identification method based on track prediction and fuzzy membership evaluation is proposed. A polynomial fitting method is used to speculate the past and future tracks. Position, course, and speed are selected as the fuzzy factors. The membership degree of each fuzzy factor between the predicted track is calculated by ridge fuzzy membership function and weighted to obtain the membership of a single moment. A weighting function is constructed to calculate the comprehensive membership degree and finally the threshold is set to determine whether the two tracks are correlated. In simulation experiments, the precision and the recall rate of the proposed method is higher than 90% and 80% respectively, which outperforms the traditional method. In order to further investigate the applicability of the proposed method, the radar monitoring data of vessel tracks under different scenarios are simulated, including stable scenarios, speed change and course change scenarios during the interruption interval. Simulation results show that the proposed method provides an effective way to solve the problem of correlation identification of interrupted tracks in the cross environment of non-cooperative vessels.

Key words: interrupt track, track correlation, track prediction, ridge-type fuzzy membership degree

中图分类号:

TN953

陈兆彤, 陈江平, 潘励. 基于模糊隶属度的船舶中断航迹关联识别方法[J]. 应用科学学报, 2023, 41(2): 296-310.

CHEN Zhaotong, CHEN Jiangping, PAN Li. Identification Method for Vessel Interrupt Track Correlating Based on Fuzzy Membership Degree[J]. Journal of Applied Sciences, 2023, 41(2): 296-310.

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