连续波雷达通常采用长时间相参累积技术来增强对无人机的探测能力。然而,无人机具有高机动性,在做长时间相参积累时易发生距离徙动和多普勒频率徙动,导致雷达检测性能衰退。针对此问题,文中提出了一种基于快时间keystone变换和分数阶傅里叶变换(FrFT)的连续波雷达无人机检测算法,通过快时间keystone变换校正距离徙动,采用FrFT补偿多普勒频率徙动,进而完成相参积累和检测。不同于传统keystone变换作用于快时间域,快时间keystone变换作用于距离频率域,这是连续波雷达信号模型的特性导致的。仿真分析显示,文中算法可良好地平衡检测性能和计算量之间的矛盾;并采用雷达实测数据验证了所提算法的有效性。

Continuous-wave radars normally apply the long-time coherent integration technique to enhance the detection performance for unmanned aerial vehicles (UAVs). However, due to the maneuverability of an unmanned aerial vehicle (UAV), range migration(RM) and Doppler frequency migration (DFM) can easily happen in the process of long-time coherent integration, thus resulting in a degradation in radar detection performance. To solve this problem, a novel algorithm based on the fast-time keystone transform and the fractional Fourier transform (FrFT) is proposed in this paper for UAV detection via a continuous-wave radar. The proposed algorithm eliminates the RM via the fast-time keystone transform and compensates the DFM through the FrFT, achieving coherent integration and target detection. Different from the traditional keystone transform that operates on the fast-time domain, the fast-time keystone transform operates on the range-frequency domain, because of the characteristics of the signal model of continuous-wave radars.Simulation results demonstrate that the proposed algorithm can strike a good balance between the radar detection performance and the computational cost. Finally, we use the real-measured data to verify the effectiveness of the proposed algorithm.

TN957;V279