A Phasor Analysis and EKF-based Accuracy Enhancement Method for LFMCW Radar Displacement Monitoring

Abstract Bridge dynamic displacement is crucial for structural safety assessments. Ground-based LFMCW radar systems offer high-precision, non-contact measurements for multi-target displacement monitoring. However, their accuracy is often compromised by variations in the signal-to-noise ratio (SNR), caused by such factors as echo energy fluctuations, environmental disturbances, and system limitations. To address these issues, we propose an enhanced method combining Phasor Analysis (PA) with adaptive Extended Kalman Filtering (EKF). PA tracks phasor amplitudes to assess phase errors and quantify the SNR at measurement targets, while EKF adaptively suppresses noise based on dynamic SNR analysis, offering a more targeted approach than traditional denoising techniques. While existing phasor offset correction methods address static clutter interference, our approach further improves displacement accuracy by applying SNR-driven EKF filtering to real-time, adaptive noise suppression. Validation through sliding table tests and field trials on the G35 Bridge of the Lianhuo Expressway demonstrates the method's effectiveness in enhancing measurement precision and robustness. This radar-specific approach provides a low-complexity and practical solution for bridge health monitoring, outperforming conventional techniques.