DE-optimized EKF for bridge deformation monitoring using GNSS and accelerometers

Abstract Bridge deformation monitoring is essential for safeguarding infrastructure against dynamic loads. However, conventional Kalman filter (KF) fusion methods suffer from suboptimal noise covariance tuning that lacks global optimization under non-stationary conditions. To address these issues, this study proposes a novel differential evolution-enhanced extended KF (DE-EKF) method, which combines global optimization techniques with traditional filtering models for improved sensor fusion. This method leverages the DE algorithm to optimize the noise covariance matrices dynamically, thereby enhancing the adaptability of the EKF to varying measurement conditions and ensuring more accurate fusion of global navigation satellite systems and accelerometer data. Experimental results demonstrate that DE-EKF significantly outperforms existing models, with root mean square error and mean absolute error reductions of 68.9% and 54.1% compared to unscented KF respectively, underscoring its superior accuracy, stability, and robustness in real-world applications. This method provides a promising direction for advancing multi-sensor data fusion in deformation monitoring, with the potential for broader applications in geodetic systems, structural health monitoring, and large-scale infrastructure management.