Uncertainty Minimization Technique for Joint Input-State Estimation Using Dummy Measurements

Joint input-state estimation algorithm is a Kalman filtering based technique that can be used for the identification of forces applied to a structure together with the unmeasured response quantities of interest. However, when applying this technique, displacement or strain measurements have to be used in order to guarantee the stability conditions. This paper presents a joint input-state estimation algorithm that uses dummy displacement or strain measurements to yield stable results. The quantification of the estimation uncertainty originating from the errors of using dummy measurements is elaborated in this paper. As joint input-state estimation is an on- line algorithm, this paper also provides an updating method to minimize the estimation uncertainty during the real-time estimation. This uncertainty minimization technique that uses dummy measurements in joint input-state estimation is verified using numerical simulations.

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