Three-dimensional bridge displacement reconstruction through Kalman filter fusion of multi-track InSAR observations

This paper presents a novel and scalable framework for the monitoring of civil structures and infrastructures, particularly bridges, using multi-track InSAR data within a recursive Kalman Filter (KF) approach. While InSAR is a well-established technique for detecting millimetric ground displacements, its direct application to Structural Health Monitoring (SHM) is often limited by the one-dimensional nature of Line-of-Sight (LOS) measurements and the need for on-site instrumentation to achieve three-dimensional reconstructions. The proposed framework mitigates these constraints by introducing a data-fusion procedure that combines LOS observations from multiple geometries with environmental and structural information, without requiring additional sensors on site. The framework is demonstrated on the A21 highway viaduct over the Po River, Italy, where ascending and descending EGMS datasets are integrated using the KF together with temperature data. The reconstructed displacements show close agreement with GNSS measurements, confirming the capability of the proposed approach to provide reliable 3D structural information using fully remote data sources.