A Fast Synthetic-Frame Method for MIMO Radar Global Imaging

This paper introduces a novel method for synthesizing high-resolution MIMO radar images on a frame-by-frame basis, specifically designed for scanning environments from moving platforms. Leveraging higher solution millimeter-wave radar, the approach employs Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to cluster point clouds into distinct objects for each image frame. Subsequently, image frames are aligned using the Iterative Closest Point (ICP) algorithm to compute the relative motion between them. These frames are then combined to reconstruct a comprehensive image of the entire scanned environment. Notably, this method achieves high-resolution imaging without the need for supplementary hardware such as cameras, GPS, or IMU systems, thereby significantly reducing costs. The effectiveness and efficiency of this approach make it particularly valuable for environmental sensing and map construction in robotics and autonomous driving applications.