Environmental-Awareness-Based Automatic Correction Algorithm for Radar Track Errors

Radar measurements could be subject to deviations in target position precision caused by systematic errors and other factors. Measurement errors in radar can prevent trajectories from different sensors for the same target from being effectively fused, thereby affecting the operational efficiency of the combat system. Correcting the radar's measurement error using reference sources is an effective means to improve measurement accuracy. However, when the radar measurement error is comparable to the target spacing and false alarms or missed detections occur simultaneously, traditional target association methods are prone to incorrect associations in such scenarios, leading to the failure of error correction. To address this issue, this paper proposes an environment-aware method for searching track error correction reference sources. This method perceives the density of targets in the scene, automatically identifying reliable reference sources, and completes error extraction and correction. Verified through target simulation and actual measurement data, this method can effectively correct radar measurement errors and significantly improve target measurement accuracy.