摘要
在空间跟踪船海上多目标跟踪场景中,目标船的航迹关联一直是严峻的挑战。由于海洋表面是一个高度动态的环境,海杂波的存在有很强的不规则性和随机性,使得探测到的目标点中夹杂着大量的不真实点。对此,提出了一种基于运动特征的多目标航迹生成方法,该方法包括预处理和航迹段关联两个关键环节。在预处理阶段,通过目标船数据的经纬度、速度和航向角的阈值剔除轨迹异常点,并采用基于B-spline的采样-分段-插值方法,增强目标轨迹的完整性、连续性和平滑性;在航迹段关联阶段,设计了一种结合运动特征和时间约束的多目标航迹关联策略。实际海域实验结果表明,该方法能够有效提高航迹生成的准确性和鲁棒性。
In the maritime multi-target tracking context of space tracking vessels,the trajectory correlation of target ships has remained a formidable challenge.Owing to the highly dynamic nature of the oceanic environment and the irregularity as well as randomness of sea clutter,the detected target points frequently encompass a multitude of false detections.This paper presents a motion-feature-based multi-target trajectory generation approach,which comprises two crucial stages:preprocessing and trajectory segment association.In the preprocessing stage,trajectory outliers are eliminated by imposing threshold constraints on latitude,longitude,speed,and heading angle,followed by a B-spline-based sampling-segmentation-interpolation method to enhance the completeness,continuity,and smoothness of the target trajectories.In the trajectory segment association stage,a multi-target tra-jectory association strategy is formulated,integrating motion features and temporal constraints.Experimental outcomes in real maritime scenarios illustrate that the proposed method substantially enhances the accuracy and robustness of trajectory generation.
作者
张浩然
王桂玲
ZHANG Haoran;WANG Guiling(Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data,North China University of Technology,Beijing 100144,China;School of Information,North China University of Technology,Beijing 100144,China)
出处
《计算机科学》
北大核心
2025年第8期154-161,共8页
Computer Science
关键词
雷达航迹关联
航迹插值
航迹生成
运动特征
多目标
Radar track correlation
Track interpolation
Track generation
Motion characterization
Multi-target
作者简介
ZHANG Haoran,born in 2000,postgraduate.His main research interest is Spatiotemporal data analysis and mining.(zhr000405@163.com);通信作者:WANG Guiling,born in 1978,Ph.D,professor,is a member of CCF(No.17649M).Her main research interests include data integration,services computing and largescale streaming.(wangguiling@ncut.edu.cn).
