Automotive Radar Ghost Target Detection With Physics‐Aware Graph Neural Network
Guanhua Ding, Shuo Zhang, Ran Liu, Jinping SunABSTRACT
Multipath propagation in automotive radar produces ghost targets that can mislead perception systems in autonomous driving. This letter proposes a physics‐aware graph neural network (PAGNN) that exploits multipath propagation geometry for radar ghost detection. PAGNN operates in two stages: a GNN‐based affinity clustering stage that groups radar points into target‐level clusters, and a classification stage that identifies ghosts through graph convolution and physics‐informed attention modules. Experiments on real‐world automotive radar data demonstrate that PAGNN outperforms existing methods, improving mean average precision by 15.83 percentage points over the PointNet++ baseline. These results highlight the effectiveness of embedding physical models into graph‐based learning for radar perception.