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Implementation of a GNU radio and python FMCW radar toolkit

机译:GNU无线电和Python FMCW雷达工具包的实现

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The use of GNU Radio in order to explore FMCW radar is growing rapidly, where it is used for radar signal processing. In this paper we implement FMCW surveillance radar for drone detection on SDR using GNU Radio and the USRP B210. The requirement was to design and implement an FMCW surveillance radar to detect a drone with a radar cross-section of 0.1 m2 and a maximum range of 150 m for the purpose of point detection. The signal processing takes the beat frequency which has pulse compression gain and performs coherent integration. It windows and displays the results on a range Doppler map using two-dimensional fast Fourier transform. The pulse repetition frequency of the waveform is selected as 2200 Hz with a chirp bandwidth of 28 MHz, allowing for resolving a maximum velocity of 30 m/s unambiguously, which is the typical maximum speed of a small drone. Experiments were conducted using a human being and a car target as a drone was not available. The B210 induced a phase drift in the results which causes a Doppler shift. The phase drift was resolved by creating a phase equalization matrix, which was used to correct the phase drift in real-time. Verification of the designed radar system yielded a 63% probability of detection and probability of false alarm of 1.7 × 10-6.
机译:为了探索FMCW雷达,GNU Radio的使用正在迅速增长,用于雷达信号处理。在本文中,我们使用GNU Radio和USRP B210实施FMCW监视雷达,以对SDR进行无人机检测。要求是设计和实施FMCW监视雷达,以检测雷达横截面为0.1 m 2 且最大射程为150 m的无人机,以进行点检测。信号处理采用具有脉冲压缩增益并执行相干积分的拍频。它使用二维快速傅立叶变换在范围多普勒地图上显示窗口并显示结果。波形的脉冲重复频率选择为2200 Hz,线性调频带宽为28 MHz,可以明确解析最大速度30 m / s,这是小型无人机的典型最大速度。实验是使用人类进行的,由于无人驾驶飞机无法使用汽车目标。 B210在结果中引起相位漂移,从而引起多普勒频移。通过创建一个相位均衡矩阵来解决相位漂移问题,该矩阵用于实时校正相位漂移。对设计雷达系统的验证得出63%的检测概率和1.7×10 -6 的误报概率。

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