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Detection of runway boundary in ATC ground radar

机译:ATC地面雷达跑道边界的检测

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The radar environment is usually nonstationary and an efficient adaptation requires real-time exploitation of a priori knowledge concerning the environment surrounding the radar. In an airport contest, the radar scenario is heavily influenced by the inevitable presence of interferences, the background clutter, but other interferences (presence of vehicles and unwanted signs on the runway) contribute to make difficult the target detection. In particular, the paper addresses the detection of the runway boundary in ATC (Air Traffic Control) ground radar signal which can be not well defined due to the grass growth in different period in the islands near the runways. The grass growth can cause critical situation because the signal level of the grass zones is equal to that of the asphalt and it needs a visual analysis by a human operator to determine the presence of objects and targets in this clutter area. An active contour search algorithm (Snake algorithm) is used for detecting the runway boundary in SMR radar images. In our application a standard Snake algorithm is adapted and it is possible to obtain a reasonable approximation of the actual contour, which greatly reduces drawbacks related to the presence of the local minima. The performance of the active contour algorithm is tested on a set of radar images extracted from SMR radar images acquired in Italian airports.
机译:雷达环境通常是不稳定的,有效的适应需要实时利用有关雷达周围环境的先验知识。在机场竞赛中,雷达场景会受到不可避免的干扰,背景杂波的严重影响,但是其他干扰(车辆的存在和跑道上不想要的标志)也会导致目标检测变得困难。特别是,本文讨论了在ATC(空中交通管制)地面雷达信号中检测跑道边界的问题,由于跑道附近岛屿上不同时期的草生长,因此无法很好地定义该边界。草的生长会导致严重情况,因为草区域的信号电平等于沥青的信号电平,并且需要操作人员进行视觉分析以确定该杂物区域中是否存在物体和目标。主动轮廓搜索算法(Snake算法)用于检测SMR雷达图像中的跑道边界。在我们的应用中,采用了标准的Snake算法,可以合理地逼近实际轮廓,从而大大减少了与局部极小值相关的缺陷。在从意大利机场获取的SMR雷达图像中提取的一组雷达图像上测试了主动轮廓算法的性能。

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