一种毫米波雷达快速高分辨三维点云成像方法

针对现有毫米波雷达三维点云成像方法难以兼顾高分辨性能和运算速度的问题,提出一种结合自适应网格进化和稀疏渐近最小方差(SAMV)的快速高分辨三维点云成像方法。 该方法首先在快速傅里叶变换(FFT)获得距离像的基础上利用恒虚警率(CFAR)检测目标的距离单元,以此估计目标的距离,然后对有目标的各距离单元使用“划分粗网格-功率谱估计-检测-网格细化”的自适应网格进化方法,得到感兴趣的方位-俯仰角细化网格,对这些细化网格估计功率谱后再次进行检测以快速得到目标的三维点云像。 上述过程中,网格分裂前后的功率谱估计均使用 2D SAMV 方法,以提高点云像中的角度信息的精确度、分辨率。 实验结果表明,该方法在保持 4°的角度分辨率的同时,将生成三维点云的速度提升至 2D SAMV 方法的 10 倍左右。

Fast high-resolution 3D point cloud imaging method of millimeter wave radar

Aiming at the problem that the existing three-dimensional point cloud imaging methods for millimeter-wave radar are difficult togive consideration to both high resolution performance and computing speed, a fast high resolution three-dimensional point cloud imagingmethod combining adaptive grid evolution and SAMV is presented. Based on the range image obtained by FFT, the range bin of thetarget detected by CFAR is used to estimate the distance of the target. Then, the adaptive mesh evolution method of dividing coarsemesh-power spectrum estimation-detection-mesh refinement is used for the azimuth-elevation angle of each range bin with the target toobtain the refined mesh of interest. After estimating the power spectrum for these thinned grids, the detection is performed again toquickly obtain the three-dimensional point cloud image of the target. The power spectrum of the grid is estimated by using 2D SAMVbefore and after grid splitting, which improves the information accuracy and resolution. The results show that the proposed method canincrease the speed of generating three-dimensional point clouds to about 10 times that of the 2D SAMV method while maintaining theangular resolution of 4 degrees.