University of St. Andrews, SUPA School of Physics & Astronomy, North Haugh, St. Andrews, KY16 9SS, Fife, Scotland.
Sci Rep. 2018 Nov 26;8(1):17396. doi: 10.1038/s41598-018-35880-9.
Due to the substantial increase in the number of affordable drones in the consumer market and their regrettable misuse, there is a need for efficient technology to detect drones in airspace. This paper presents the characteristic radar micro-Doppler properties of drones and birds. Drones and birds both induce micro-Doppler signatures due to their propeller blade rotation and wingbeats, respectively. These distinctive signatures can then be used to differentiate a drone from a bird, along with studying them separately. Here, experimental measurements of micro-Doppler signatures of different types of drones and birds are presented and discussed. The data have been collected using two radars operating at different frequencies; K-band (24 GHz) and W-band (94 GHz). Three different models of drones and four species of birds of varying sizes have been used for data collection. The results clearly demonstrate that a phase coherent radar system can retrieve highly reliable and distinctive micro-Doppler signatures of these flying targets, both at K-band and W-band. Comparison of the signatures obtained at the two frequencies indicates that the micro-Doppler return from the W-band radar has higher SNR. However, micro-Doppler features in the K-band radar returns also reveal the micro-motion characteristics of drones and birds very effectively.
由于消费市场上价格实惠的无人机数量大幅增加，以及这些无人机令人遗憾的滥用，因此需要高效的技术来检测空域中的无人机。本文介绍了无人机和鸟类的特征雷达微多普勒特性。无人机和鸟类都由于其螺旋桨叶片的旋转和翅膀的拍打而分别产生微多普勒特征。这些独特的特征可用于区分无人机和鸟类，并分别对它们进行研究。这里，展示和讨论了不同类型的无人机和鸟类的微多普勒特征的实验测量结果。这些数据是使用两种工作在不同频率的雷达采集的; K 波段（24GHz）和 W 波段（94GHz）。为了数据采集，使用了三种不同型号的无人机和四种不同大小的鸟类。结果清楚地表明，相参雷达系统可以在 K 波段和 W 波段都可靠地获取这些飞行目标的高度独特的微多普勒特征。在两个频率上获得的特征的比较表明，来自 W 波段雷达的微多普勒回波具有更高的 SNR。然而，K 波段雷达回波中的微多普勒特征也非常有效地揭示了无人机和鸟类的微运动特征。
