基于无人水面艇感知网的目标船航迹关联与数据融合
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1.浙江海洋大学;2.舟山市质量技术监督检测研究院

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基金项目:

舟山市科技局项目(2019C21025),浙江省市场监督管理局(20200132)。


Target Ship Track Association and Data Fusion Based on Unmanned Surface Vehicle Perception Network
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1.Zhejiang Ocean University;2.Zhoushan Institute of Calibration and Testing for Quality and Technology Supervision

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    摘要:

    针对现有无人水面艇(Unmanned Surface Vehicle,USV)航行过程中感知周围航行目标出现的数据源单一、数据延迟、数据丢失问题,提出一种基于无人艇搭载的航海雷达和全球定位系统(Global Positioning System,GPS)数据源的USV海上航行目标感知数据融合方法。基于最小误差法提出一种雷达原始图像数据解析算法,并通过数据剔除、时间空间统一方法完成对目标数据预处理,构建了基于欧氏距离和马氏距离的航迹关联算法模型、基于层次分析法和专家评价法的融合数据权重分配模型。同时,开展了USV实验研究,验证了整体融合方法。结果表明:目标原始数据预处理方法合理可靠,融合算法稳定可信,可为USV海上航行目标感知、安全航行及快速避碰提供技术和算法支持。

    Abstract:

    In view of the existing problems of single data source, data delay and data interruption in sensing the surrounding navigation targets during the navigation of the existing USV, a USV maritime navigation target sensing data fusion method based on radar and GPS data source is proposed. Based on the minimum error method, a radar original image data analysis algorithm is proposed, and the target data is preprocessed through data elimination and time-space unification methods. The track association algorithm model based on Euclidean distance and Mahalanobis distance and the fusion data weight distribution model based on analytic hierarchy process and expert evaluation method are constructed, The overall fusion method is verified by USV measured navigation data. The results show that the target raw data preprocessing method is reasonable and reliable, and the fusion algorithm is stable and reliable, which can provide technical and theoretical support for USV maritime navigation target perception, safe navigation and rapid collision avoidance.

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历史
  • 收稿日期:2021-12-14
  • 最后修改日期:2022-01-06
  • 录用日期:2022-01-10
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