船舶横纵摇与航向控制存在强耦合性，在超远距离跟踪中获取的航迹数据会出现时滞性，而单级跟踪方法易因航迹数据缺失产生轨迹断裂，使得航迹跟踪的可信度降低。为此，提出融合大数据同态滤波的船舶超远航迹跟踪方法。利用大数据技术中的改进DBSCAN算法聚类船舶航行运动状态数据，利用同态滤波算法去除聚类后的数据噪声，增强轨迹数据；依据增强后的轨迹数据，经由两级跟踪算法跟踪船舶超远航迹，解决因航迹数据缺失产生轨迹断裂。测试结果显示，该方法具备较好的聚类效果，聚类后数据重合度系数值均在0.92以上；能够可靠完成航迹跟踪，不同航迹跟踪可信度值均在0.93以上。
There is a strong coupling between ship roll and pitch control and heading control, and the trajectory data obtained in ultra long distance tracking may have time delay. Single level tracking methods are prone to trajectory breakage due to missing trajectory data, which reduces the credibility of trajectory tracking. Therefore, a ship ultra long distance trajectory tracking method that integrates big data homomorphic filtering is proposed. Using the improved DBSCAN algorithm in big data technology to cluster ship navigation motion state data, using homomorphic filtering algorithm to remove noise from the clustered data and enhance trajectory data. Based on the enhanced trajectory data, the ship's ultra long trajectory is tracked using a two-level tracking algorithm to solve trajectory breakage caused by missing trajectory data. The test results show that this method has a good clustering effect. The coefficient of data overlap after clustering is all above 0.92. It successfully completed the trajectory tracking, and the credibility values of different trajectory tracking are all above 0.93.
2026,48(7): 101-105 收稿日期：2025-4-18
DOI：10.3404/j.issn.1672-7649.2026.07.017
分类号：U675.7；TP391
作者简介：江忠涛(1983-),男,高级工程师,研究方向为计算机应用
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