A Low-Complexity Forward–Backward Filtering Algorithm for Real-Time GNSS Deformation Monitoring at the Edge

Real-time Global Navigation Satellite System (GNSS) deformation monitoring is crucial for structural safety but is challenged by long-term, high-amplitude noise and trend-like anomalies. To address these issues, we propose a low-complexity forward–backward reliable filtering algorithm (FBRFF) tailored for edge environments. FBRFF integrates trend-aware correction and confidence-based fusion within a sliding window framework to effectively suppress non-stationary disturbances while preserving true deformation signals. Its architecture compensates for the inadequate performance of existing filters under persistent large-amplitude noise, enabling early anomaly correction before deformation analysis. In addition, it significantly reduces system computational load and complexity when processing massive multi-source data, while allowing easy integration with other filtering algorithms for enhanced robustness. Experiments using real-world GNSS data from the Usnisa Palace monitoring project validate that FBRFF improves positioning accuracy by up to 82% over baseline methods and maintains real-time responsiveness on resource-constrained platforms. These results demonstrate that FBRFF provides a lightweight, robust, and scalable solution for real-time GNSS monitoring, offering practical value for early-warning systems and infrastructure safety management.