Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain

Anti-collision assistance, integral to the current drive toward increased vehicular autonomy, relies heavily on precise detection and localization of moving targets in the vehicle’s vicinity. A crucial step toward achieving this is the removal of static objects from the scene, thereby enhancing the detection and localization of dynamic targets—a pivotal aspect in augmenting overall system performance. In this article, we propose a static background removal algorithm tailored for automotive scenarios, designed for common frequency-modulated continuous wave (FMCW) radars. This algorithm effectively eliminates reflections corresponding to static backgrounds from radar images through a two-step process: 4-D radar imaging and filtering in the azimuth-elevation-Doppler domain. Our proposed approach is underpinned by a model customized for FMCW radar signals, incorporating a time-division multiplexing (TDM)-based multiple-input multiple-output (MIMO) scheme on the nonuniform radar array. Furthermore, our filtering process requires knowledge of the 3-D radar ego-motion velocity, typically obtained from an external sensor. To address scenarios where such sensors are unavailable, we introduce a self-contained 3-D ego-motion estimation approach. Finally, we evaluate the performance of our algorithm using both simulated and real-world data, analyzing its sensitivity and time complexity in comparison to established baselines.