Radar Data Tracking Using Minimum Spanning Tree-Based Clustering Algorithm
Radar Data Tracking Using Minimum Spanning Tree-Based Clustering Algorithm
复制标题
使用基于最小生成树的聚类算法进行雷达数据跟踪
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Bassam Musaar
中科院分区：
文献类型：
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作者：
Chunki Park;Hak;Bassam Musaar
本文讨论了一种关联和更新来自多个雷达站点的飞机跟踪数据的新方法。该方法提供了增强的飞机航迹精度和时间同步，与现代空中交通管理分析和模拟工具兼容。与必须假设雷达数据中飞机数量的现有方法不同，该方法不需要此类先验知识。虽然商用飞机以模式 3 应答器代码的形式提供在雷达数据中捕获的 ID 标签，但通用航空通常缺乏此类应答器，这妨碍了使用感测到的代码数量来计算数据中的飞机数量。为了应对这一挑战，提出了一种使用聚类算法跟踪未知数量的身份不明飞机的方法。本文提出了一种在连续时间帧之间关联飞机并重新确定这些车辆的轨迹的方法。提供了评估算法并证明其可行性的实验结果。
This paper discusses a novel approach to associate and rene aircraft track data from multiple radar sites. The approach provides enhanced aircraft track accuracy and time synchronization that is compatible with modern air trac management analysis and simulation tools. Unlike existing approaches where the number of aircraft in the radar data must be assumed, this approach requires no such prior knowledge. While commercial aircraft provide ID tags captured in the radar data in the form of Mode 3 transponder codes, general aviation often lacks such transponders, which precludes using the number of codes sensed to count the number of aircraft in the data. To meet this challenge, an approach to track an unknown number of unidentied aircraft using a clustering algorithm is proposed. The paper presents a method to relate aircraft between consecutive time frames and rene the trajectories of those vehicles. Experimental results from evaluating the algorithm and demonstrating its viability are provided.