Abstract: Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular position estimation. A machine-learned module analyzes complex range data generated to estimate angular positions of objects. The machine-learned module is implemented using a multi-stage architecture. In a local stage, the machine-learned module splits the complex range data into different range intervals and separately processes subsets of the complex range data using individual branch modules. In a global stage, the machine-learned module merges the feature data generated from the individual branch modules using a symmetric function and generates angular position data. By using machine-learning techniques and processing the complex range data directly, the radar system can achieve higher angular resolutions compared to other radar systems that utilize other techniques, such as analog or digital beamforming.