Data fusion based EKF-UI for real-time simultaneous identification of structural systems and unknown external inputs

• Propose an EKF-UI using fusion of partial acceleration and displacement measurements.
• The EKF-UI is a direct extension of the conventional EKF.
• The EKF-UI prevents the drifts in the estimated results by previous approaches.
• The KF-UI is efficient for real time identification of structures and unknown inputs.

The extended Kalman filter (EKF) is an efficient tool for structural health monitoring and vibration control due to its superiority. However, the conventional EKF approach is only applicable when the information of external inputs to structures is available. Some improved methodologies with different complexities have been proposed in the last decade, but previous approaches based solely on acceleration measurements are inherently unstable which leads to drifts in the estimated unknown inputs and structural displacements. Although regularization schemes or post signal processing can be used to treat the drifts, they are not suitable for the real-time identification of structural systems and unknown inputs. In this paper, it is aimed to directly extend the conventional EKF for real-time simultaneous identification of structural systems and unknown external excitations. Based on the procedures of the conventional EKF, an extended Kalman filter with unknown excitations (EKF-UI) is directly derived. Moreover, data fusion of partially measured displacement and acceleration responses is applied to prevent in real time the previous drifts in the estimated structural displacements and unknown external inputs. Several numerical examples are used to demonstrate the effectiveness of the proposed EKF-UI for real-time identification of linear or nonlinear structural systems and unknown external excitations.