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A novel approach of collision assessment for coastal radar surveillance

机译：海岸雷达监视的碰撞评估新方法

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摘要

For coastal radar surveillance, this paper proposes a data-driven approach to estimate a blip's collision probability preliminarily based on two factors: the probability of it being a moving vessel and the collision potential of its position. The first factor is determined by a Directed Acyclic Graph (DAG), whose nodes represent the blip's characteristics, including the velocity, direction and size. Additionally, the structure and conditional probability tables of the DAG can be learned from verified samples. Subsequently, obstacles in a waterway can be described as collision potential fields using an Artificial Potential Field model, and the corresponding coefficients can be trained in accordance with the historical vessel distribution. Then, the other factor, the positional collision potential of any position is obtained through overlapping all the collision potential fields. For simplicity, only static obstacles have been considered. Eventually, the two factors are characterised as evidence, and the collision probability of a blip is estimated by combining them with Dempster's rule. Through ranking blips on collision probabilities, those that pose high threat to safety can be picked up in advance to remind radar operators. Particularly, a good agreement between the proposed approach and the manual operation was found in a preliminary test. (C) 2016 Elsevier Ltd. All rights reserved.
机译：对于沿海雷达监视，本文提出了一种数据驱动的方法，该方法可以基于以下两个因素来初步估计笔尖的碰撞概率：它是移动的船舶的概率和其位置的碰撞可能性。第一个因素由有向无环图（DAG）决定，其有序节点表示斑点的特征，包括速度，方向和大小。此外，可以从已验证的样本中学习DAG的结构和条件概率表。随后，可以使用人工势场模型将水路中的障碍物描述为碰撞势场，并且可以根据历史船只分布来训练相应的系数。然后，另一个因素是，通过重叠所有碰撞势场来获得任何位置的位置碰撞势。为了简单起见，仅考虑静态障碍。最终，这两个因素被表征为证据，并且通过将它们与Dempster规则相结合来估计斑点的碰撞概率。通过对碰撞概率进行排序，可以提前拾取那些对安全性构成高威胁的事件，以提醒雷达操作员。特别是，在初步测试中发现了建议的方法与手动操作之间的良好协议。 （C）2016 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Reliability Engineering & System Safety》 |2016年第11期|179-195|共17页
  • 作者

    Ma Feng; Chen Yu-wang; Huang Zi-chao; Yan Xin-ping; Wang Jin;

  • 作者单位

    Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan, Hongshan, Peoples R China|Univ Manchester, Decis & Cognit Sci Res Ctr, Manchester M15 6PB, Lancs, England|Liverpool John Moores Univ, Liverpool Logist Offshore & Marine LOOM Res Ins, Liverpool L3 3AF, Merseyside, England;

    Univ Manchester, Decis & Cognit Sci Res Ctr, Manchester M15 6PB, Lancs, England;

    Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan, Hongshan, Peoples R China|Natl Engn Res Ctr Water Transportat Safety WTS, Wuhan, Peoples R China;

    Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan, Hongshan, Peoples R China|Natl Engn Res Ctr Water Transportat Safety WTS, Wuhan, Peoples R China;

    Liverpool John Moores Univ, Liverpool Logist Offshore & Marine LOOM Res Ins, Liverpool L3 3AF, Merseyside, England;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Collision probability; Bayesian Network; Artificial Potential Field; Marine radar; Nonlinear optimisation; Dempster's rule;

    机译：碰撞概率贝叶斯网络人工势场海洋雷达非线性优化邓斯特法则;

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