Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements

• We developed a new data fusion algorithm for dynamic displacement estimation.
• It autonomously combines acceleration and intermittent displacement measurements.
• It explicitly considers acceleration measurement error.
• Dynamic experiments and displacement estimation are conducted for its verification.

Addressing the importance of displacement measurement of structural responses in the field of structural health monitoring, this paper presents an autonomous algorithm for dynamic displacement estimation from acceleration integration fused with displacement data intermittently measured. The presented acceleration integration algorithm of multi-rate Kalman filtering distinguishes itself from the past study in the literature by explicitly considering acceleration measurement bias. Furthermore, the algorithm is formulated by unique state definition of integration errors and error dynamics system modeling. To showcase performance of the algorithm, a series of laboratory dynamic experiments for measuring structural responses of acceleration and displacement are conducted. Improved results are demonstrated through comparison between the proposed and past study.