Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2659 - 2659

Published: April 23, 2025

To accurately capture the dynamic displacement of super-tall buildings under complex conditions, this study proposes a data fusion algorithm that integrates NRBO-FMD optimization with Adaptive Robust Kalman Filtering (ARKF). The method preprocesses GNSS and accelerometer to mitigate multipath effects, unmodeled errors, high-frequency noise in signals. Subsequently, ARKF fuses preprocessed achieve high-precision reconstruction. Numerical simulations varying conditions validated algorithm’s accuracy. Field experiments conducted on Hairong Square Building Changchun further demonstrated its effectiveness estimating three-dimensional displacement. Key findings are as follows: (1) significantly reduced while preserving essential signal characteristics. For data, root mean square error (RMSE) was 0.7 mm for 100 s dataset 1.0 200 dataset, corresponding signal-to-noise ratio (SNR) improvements 3.0 dB 6.0 dB. RMSE (100 s) 6.2 (200 s), 4.1 SNR gain. (2) NRBO-FMD–ARKF achieved high accuracy, values 1.9 s). Consistent PESD POSD long-term stability effective suppression irregular errors. (3) successfully fused 1 Hz overcoming limitations single-sensor approaches. yielded an 3.6 mm, 2.6 4.8 demonstrating both precision robustness. Spectral analysis revealed key response frequencies ranging from 0.003 0.314 Hz, facilitating natural frequency identification, structural stiffness tracking, early-stage performance assessment. This shows potential improving integration health monitoring. Future work will focus real-time predictive estimation enhance monitoring responsiveness early-warning capabilities.

Language: Английский