Indoor Radar Point Cloud Ghost Elimination Method Based on Point Cloud Cluster Occlusion

Due to the large number of targets and the close detection distance in indoor environments, a significant amount of ghost target points often appears in the imaging results of millimeter-wave (mmWave) radar point clouds. These ghost points can distort the point cloud images and greatly reduce their readability. Nowadays, ghost elimination methods may affect the imaging accuracy of other targets, and the algorithm is complex, which is not suitable for application in narrow indoor environments. To mitigate interference caused by ghost image target points on already pointed cloud images, this article proposes a ghost image removal method called double-parameter elimination of ghost images (DPEGIs) based on point cloud clustering. This method determines the occlusion relationship between each point cloud cluster by setting two key parameters. According to the relative scattering coefficient of the point cloud cluster, the ghost can be effectively removed while retaining the non-line-of-sight (NLOS) targets. The extensive verification using the indoor measured data demonstrates that the proposed ghost elimination algorithm is capable of handling common indoor scenes while preserving over 90% of details with high accuracy, robustness, efficiency, and real-time processing capability.