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• 几何设计与计算 • 上一篇    下一篇

终端区航迹簇的中心航迹提取方法研究

摘 要:为了有助于飞行计划的准确制定和管制员工作负荷的减轻,提出了一种#br# 基于计算几何的中心航迹提取方法。首先由雷达航迹数据生成三维点云模型,然后沿飞行方#br# 向对原始点云进行自适应采样,接下来利用采样得到的代表点集建立图结构并进行优化,最#br# 后先后通过均匀化处理以及光滑处理,生成最终的中心航迹曲线。真实数据的实验结果表明:#br# 该方法能够准确地提取终端区航迹簇的中心航迹,并有效识别其特征点,同时本文方法具有#br# 一定的鲁棒性。#br# 关 键 词:终端区;航迹簇;中心航迹提取;计算几何   

  • 出版日期:2015-06-30 发布日期:2015-05-05

esearch on Central Trajectory Extraction of Trajectory Cluster in Terminal Area

Abstract: In order to create a flight plan precisely and reduce the overload of controller, a#br# novel method of central trajectory extraction based on computational geometry is proposed.#br# Firstly, a 3D point cloud model is generated from the radar data, and then a set of sampling points#br# is calculated through an adaptive sampling method along the flight direction. The graph which is#br# established from the sampling points is optimized in the next step. Finally, the central trajectory#br# curve is generated by uniformization and smooth processings successively. Experiments on real#br# data demonstrate that the proposed method can accurately extract the central trajectory of#br# trajectory cluster in terminal area, and recognize the feature points efficiently. Meanwhile, the#br# proposed method is robust.#br# Key words: terminal area; trajectory cluster; central trajectory extraction; computational#br# geometry   

  • Online:2015-06-30 Published:2015-05-05

摘要: 为了有助于飞行计划的准确制定和管制员工作负荷的减轻,提出了一种
基于计算几何的中心航迹提取方法。首先由雷达航迹数据生成三维点云模型,然后沿飞行方
向对原始点云进行自适应采样,接下来利用采样得到的代表点集建立图结构并进行优化,最
后先后通过均匀化处理以及光滑处理,生成最终的中心航迹曲线。真实数据的实验结果表明:
该方法能够准确地提取终端区航迹簇的中心航迹,并有效识别其特征点,同时本文方法具有
一定的鲁棒性。

关键词: 终端区, 航迹簇, 中心航迹提取, 计算几何

Abstract: In order to create a flight plan precisely and reduce the overload of controller, a
novel method of central trajectory extraction based on computational geometry is proposed.
Firstly, a 3D point cloud model is generated from the radar data, and then a set of sampling points
is calculated through an adaptive sampling method along the flight direction. The graph which is
established from the sampling points is optimized in the next step. Finally, the central trajectory
curve is generated by uniformization and smooth processings successively. Experiments on real
data demonstrate that the proposed method can accurately extract the central trajectory of
trajectory cluster in terminal area, and recognize the feature points efficiently. Meanwhile, the
proposed method is robust.

Key words: terminal area, trajectory cluster, central trajectory extraction, computational
geometry