A dual Kalman filter approach for state estimation via output-only acceleration measurements
Abstract
A dual implementation of the Kalman filter is proposed for estimating the unknown input and states of a linear state-space model by using sparse noisy acceleration measurements. The successive structure of the suggested filter prevents numerical issues attributed to un-observability and rank deficiency of the augmented formulation of the problem. Furthermore, it is shown that the proposed methodology furnishes a tool to avoid the so-called drift in the estimated input and displacements commonly encountered by existing joint input and state estimation filters. It is shown that, by fine-tuning the regulatory parameters of the proposed technique, reasonable estimates of displacements and velocities of structures can be accomplished.
- Publication:
-
Mechanical Systems and Signal Processing
- Pub Date:
- August 2015
- DOI:
- 10.1016/j.ymssp.2015.02.001
- Bibcode:
- 2015MSSP...60..866E
- Keywords:
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- Kalman filter;
- State estimation;
- Input estimation;
- Response prediction;
- Unknown input