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Improving The Accuracy Of Selection Of Bird Radar Echoes Against A Background Of Atomized Clouds And Atmospheric Inhomogeneities

机译:在雾化云和大气非均匀性背景下提高鸟类雷达回波的选择精度

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The algorithm for bird radar echo selection was developed in Israel and has been successfully used for many years to monitor birds in periods of massive intercontinental migration in order to ensure flight safety in civil and military aviation. However, it has been found that under certain meteorological conditions the bird echo selection algorithm does not filter out false signals formed by atomized clouds and atmospheric inhomogeneities. Although the algorithm is designed to identify and sift false signals, some useful echoes from smaller birds are erroneously sifted as well.This paper presents some additional features of radar echoes reflected from atmospheric formations that can be taken into account to prevent the loss of useful bird echoes. These additional features are based on the use of polarization, fluctuation and Doppler characteristics of a reflected signal. By taking these features into account we can reduce the number of false signals and increase the accuracy of the bird echo selection algorithm. The paper presents methods for using radar echoes to identify species and sizes of birds, together with recommendations on using the data to ensure flight safety during periods of massive intercontinental bird migration.
机译:用于鸟类雷达回波选择的算法是在以色列开发的,并已成功用于许多洲际大规模迁徙期间的鸟类监测,以确保民用和军用航空的飞行安全。但是,已经发现,在某些气象条件下,鸟类回声选择算法不能过滤出雾化的云和大气不均匀性形成的虚假信号。尽管该算法旨在识别和过滤虚假信号,但也会对来自较小鸟类的有用回波进行错误筛选。本文介绍了从大气层反射的雷达回波的一些其他特征,可以将这些特征考虑在内以防止有用鸟类的损失回声。这些附加功能基于反射信号的偏振,波动和多普勒特性的使用。通过考虑这些特征,我们可以减少错误信号的数量,并提高鸟类回声选择算法的准确性。本文介绍了使用雷达回波识别鸟类的种类和大小的方法,以及在大规模洲际鸟类迁徙期间使用这些数据确保飞行安全的建议。

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