Joint Target Discrimination and Ambiguity Resolution for Airborne Radar via Physics‐Guided Spatio‐Temporal Transformer

Modern airborne intercept radars typically operate in the medium pulse repetition frequency (MPRF) mode. Measurements in this mode suffer from inherent ambiguity in both range and Doppler dimensions. Traditionally, methods based on frame‐to‐frame staggering rely on local decision mechanisms, idealised numerical rules and preset kinematic models. In complex scenarios, performance drops rapidly. To tackle this, a joint target discrimination and ambiguity resolution algorithm based on the physics‐guided spatio‐temporal transformer (PG‐STT) is proposed. Firstly, physically meaningful disambiguation hypotheses and high‐dimensional heuristic features are extracted from the ambiguous radar plots. Secondly, a spatial transformer is utilised to learn the spatial distribution of the hypotheses. Also, a temporal transformer is applied to learn global motion patterns and counteract measurement noise. Finally, target discrimination, range disambiguation and velocity disambiguation are accomplished by three linear prediction heads, respectively. Simulation results validate the effectiveness of the proposed method. The algorithm demonstrates strong capabilities in target discrimination and ambiguity resolution, even in complex scenarios with low SNR, strong clutter and manoeuvring targets. Compared with traditional methods and monolithic baseline methods, our algorithm shows significant advantages in overall performance.