Dual radar vision: A feature fusion approach for advanced object detection in IoT radar networks

60 GHz radar technology is one of the most promising movement detector solutions for Internet of Things (IoT) applications. However, challenges remain in accurately classifying different objects and detecting small objects in a multi-target scenario. This work investigates whether sensor fusion between multiple radars can enhance object detection and classification performance. A one-stage detection architecture, designed based on the features of the latest YOLO generations, is used to perform fusion based on range-Doppler (RD) maps of two non-coherent spatially separated radars. A complete physical 3D propagation simulation using ray tracing evaluates the fusion methods. This approach enables precise ground truth, as all unprocessed signal components are known, and guarantees a consistent, error-free reference. Results demonstrate that dynamic, attention-based fusion significantly improves detection and classification compared to static fusion in homogeneous and heterogeneous radar setups. • Distributed 60 GHz radar systems analyzed for propagation and backscattering effects. • One-stage YOLO-like detector adapted for processing range–Doppler radar data. • Feature fusion between two radars enhances object detection and classification. • Mid-level and attention-based fusion strategies systematically evaluated.