基于图的ATC雷达目标分类

C. Neumann, H. Senkowski
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引用次数: 1

摘要

今天的空中交通管制(ATC)雷达有望在最大范围和空间覆盖范围方面提供改进的性能,而大量不需要的小型飞行物体,如鸟类和昆虫,将不会导致错误的plot和/或错误的track2率增加。为了实现雷达功能的这些相反方面,必须对ATC初级监视雷达(PSR)的发生图进行评估,以确定是否来自需要或不需要的目标。为了这个目的,除了在ATC雷达信号和数据处理中通常实施的各种评估之外,还引入了一种新的基于签名的地块分类来处理这一具有挑战性的任务。地块分类可以识别真正的空中目标,并将它们与鸟类、“天使”、风力涡轮机和其他不需要的地块的回声区分开来。分类结果用于过滤假图和改进图跟踪关联。度量活动的结果显示了这种方法在实际操作场景中的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plot based target classification for ATC radars
Air Traffic Control (ATC) radars are expected today to provide improved performance in terms of maximum range and spatial coverage, while the huge amount of small unwanted flying objects like birds and insects shall not lead to an increased false plot1 and / or false track2 rate. To accomplish these opposite aspects of radar capabilities, the occurring plots of the ATC primary surveillance radar (PSR) have to be assessed with respect to coming from wanted or unwanted objects. In addition to the various assessments usually implemented inside the ATC radar signal and data processing for this purpose, a new signature based plot classification is introduced to handle this challenging task. The plot classification recognises true air targets and discriminates them against echoes from birds, “angels”, wind turbines and other unwanted plots. The classification results are used to filter out false plots and to improve the plot to track association. Results from measurement campaigns show the benefit of this approach in real operational scenarios.
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