A Point Cloud Improvement Method for High-Resolution 4D mmWave Radar Imagery

To meet the requirement of autonomous driving development, high-quality point cloud generation of the environment has become the focus of 4D mmWave radar development. On the basis of mass producibility and physical verifiability, a design method for improving the quality and density of point cloud imagery is proposed in this paper, including antenna design, array design, and the dynamic detection method. The utilization of apertures is promoted through antenna design and sparse MIMO array optimization using the genetic algorithm (GA). The hybrid strategy for complex point clouds is adopted using the proposed dynamic CFAR algorithm, which enables dynamic adjustment of the threshold by discriminating and calculating different scanning regions. The effectiveness of the proposed method is verified by simulations and practical experiments. Aiming at system manufacture, analysis methods for the ambiguity function (AF) and shooting and bouncing rays (SBR) tracing are introduced, and an mmWave radar system is realized based on the proposed method, with its performance proven by practical experiments.