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Signal-based online acceleration and strain data fusion using B-splines and Kalman filter for full-field dynamic displacement estimation | Synapse
March 3, 2026
Signal-based online acceleration and strain data fusion using B-splines and Kalman filter for full-field dynamic displacement estimation
AD
Aniruddha Das
Rice University
AP
Ashish Pal
Rice University
SN
Satish Nagarajaiah
Rice University
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Puntos clave
Dynamic displacement estimation improves with the use of b-splines and Kalman filters, enhancing data analysis capabilities.
Integrating acceleration and strain data can provide accurate full-field displacement measurements across various conditions.
The method utilizes data fusion techniques for real-time signal processing, reducing errors in displacement estimates.
Resulting from this analysis, investigation into optimizing sensor networks may significantly enhance structural monitoring.
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Das et al. (Tue,) studied this question.
synapsesocial.com/papers/69a7606dc6e9836116a2d2ac
https://doi.org/https://doi.org/10.1016/j.ymssp.2026.113951