Abstract
Data reconciliation is based on spatial redundancy to adjust process data to improve the quality of measurement corruption due to measurement noise. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can significantly reduce the effect of gross errors and yield less biased results. In this paper, a method is proposed to solve the robust data reconciliation problem. By using the proposed method, the robust estimator problem can be transformed into least squares estimator problem which leads to the convenience in computation. Simulation results for a linear process verify the efficiency of the proposed method.
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