RadarSimRAD: From Clean Support to Measured Response: Sensor-Conditioned Paired Schr\"odinger Bridges

Doppler radar provides complementary geometric and motion cues for autonomous perception, yet large-scale radar data remain costly to acquire at scale. LiDAR offers an attractive source for radar synthesis through accurate geometric support and mature acquisition and annotation pipelines; however, existing simulators either rely on expensive physics-based modeling or use generic learned generation that underexploits source-determined structure and leaves radar-specific measurement formation largely implicit. This limitation is particularly severe for range–azimuth–Doppler (RAD) data, where range, azimuth, and Doppler exhibit distinct response statistics and Doppler ambiguity, often leading generic generators to over-smooth weak responses or hallucinate unsupported structures. We introduce RadarSimRAD, a sensor-conditioned paired Schrödinger bridge that learns stochastic transport from an alias-aware clean geometric support endpoint to measured radar response. To model the heterogeneous formation process, we further propose a Radar Spread Adapter to explicitly capture sensor-dependent, non-exchangeable response formation across range, azimuth, and Doppler. Extensive experiments demonstrate that RadarSimRAD substantially improves radar-response fidelity over baselines, reducing axis-gradient error by 55.8% and spectral distance by 38.1% over baselines, while exhibiting zero-shot cross-sensor transfer on independently collected real-world measurements. Generated RAD further improves multiple downstream tasks under limited measured-data availability, demonstrating its practical value for radar learning in data-scarce regimes.