极地灾害区域监测的目标搜索规划与算法

doi:10.16182/j.issn1004731x.joss.22-0277

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (7): 1526-1538.doi: 10.16182/j.issn1004731x.joss.22-0277

极地灾害区域监测的目标搜索规划与算法

丁飞¹^,²^,³(), 张美楠¹^,², 庄衡衡¹^,², 马海蓉¹^,², 张登银¹^,²^,³

^1.南京邮电大学物联网学院, 江苏南京 210003
^2.南京邮电大学江苏省宽带无线通信和物联网重点实验室, 江苏南京 210003
^3.南京邮电大学通信与网络技术国家工程研究中心, 江苏南京 210003

收稿日期:2022-03-28 修回日期:2022-06-16 出版日期:2023-07-29 发布日期:2023-07-19
作者简介:丁飞(1981-)，男，副教授，博士，研究方向为智慧物联网、星地融合网络、边缘智能计算等。E-mail：dingfei@njupt.edu.cn
基金资助:
国家自然科学基金(61871446);江苏省重点研发计划(BE2020084-1);江苏省“六大人才高峰”高层次人才资助项目(DZXX-008);NUPTSF(NY220028)

Target Search Planning and Algorithm for Monitoring of Polar Disaster Areas

Fei Ding¹^,²^,³(), Meinan Zhang¹^,², Hengheng Zhuang¹^,², Hairong Ma¹^,², Dengyin Zhang¹^,²^,³

^1.School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
^2.Key Laboratory of Broadband Wireless Communication and Internet of Things of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
^3.National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2022-03-28 Revised:2022-06-16 Online:2023-07-29 Published:2023-07-19

摘要/Abstract

摘要：

为提高极地水域船舶安全航行路线规划与碰撞预警能力，提出了监测中心基于聚类和树形索引的目标搜索优化方法。以船舶当前航行区域构建灾害监测场景，引入虚拟电子围栏对监测区域进行定义；通过谱聚类算法对围栏区域进行风险等级划分，提取出高风险区域，实现目标搜索场景生成的优化；融合R-tree索引的射线算法对目标船只与围栏区域匹配关系的高效判定，从而实现目标搜索计算的优化。利用科考船舶的航行数据进行场景设定，通过仿真实验对目标搜索优化方法进行性能评估，结果表明，所提目标搜索算法性能优于传统方法，场景生成计算复杂度降低约50%，目标搜索计算效率提升约40%。

关键词: 极地, 预警, 谱聚类, R-tree索引, 射线算法

Abstract:

Aiming at improving the ability of safe navigation route planning and risk assessment of ships in polar waters, a target search model and method based on clustering and efficient indexing of monitoring center are proposed. By constructing a disaster monitoring scenario based on the current navigation area of the ship, a virtual electronic fence is introduced to define the monitoring area. Spectral clustering algorithm is used to divide the risk level of the fence area, extract high-risk areas, and optimize the generation of target search scenarios; Efficient determination of the matching relationship between the target vessel and the fence area using the ray algorithm fused with R-tree index, thereby achieving optimization of target search calculation. The performance of the target search model is evaluated and verified by using the navigation data of the polar scientific research ship. Experimental results show that the performance of the proposed target search algorithm is better than that of the traditional method, the computational complexity of scene generation is reduced by about 50%, and the target search efficiency is improved by about 40%.

Key words: polar region, early warning, spectral clustering, R-tree index, ray algorithm

中图分类号:

TP319

丁飞, 张美楠, 庄衡衡, 马海蓉, 张登银. 极地灾害区域监测的目标搜索规划与算法[J]. 系统仿真学报, 2023, 35(7): 1526-1538.

Fei Ding, Meinan Zhang, Hengheng Zhuang, Hairong Ma, Dengyin Zhang. Target Search Planning and Algorithm for Monitoring of Polar Disaster Areas[J]. Journal of System Simulation, 2023, 35(7): 1526-1538.

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极地灾害区域监测的目标搜索规划与算法

Target Search Planning and Algorithm for Monitoring of Polar Disaster Areas

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