Statistically Weighted Real-Time Burst Calibration for Monopulse Radar

This paper proposes a statistically weighted burst calibration method to ensure the long-term stability of high-precision angle estimation in monopulse radar systems. Unlike conventional pre-calibration methods, which suffer from drift accumulation and dependence on external references, the proposed approach exploits pilot signals embedded in the radar’s own transmit–receive waveform to update the calibration values in real time. This algorithm integrates a trimmed mean for outlier removal, adaptive weighting based on sample reliability, and dual clipping with an update threshold to suppress abrupt fluctuations. Simulation results under various initial error conditions and calibration burst ratios demonstrate that the proposed method achieves a stable drift tracking performance while minimizing the degradation in detection abilities.