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| - On-Line Wavelets Filtering with Application to Linear Dynamic DataReconciliation
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| Abstract
| - An on-line robust wavelet filtering is presented and applied to the dynamic data reconciliation problem viaa constrained Kalman filter approach. The wavelet filtering is used to remove outliers and provide datasmoothing prior to the reconciliation. Matrix computation is presented to facilitate the implementation ofdiscrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) for on-line filtering. Anendpoint correction method is presented to overcome the endpoint effect that is caused by wavelet filteringon an on-line moving data window. The filtered outputs are treated as the output measurements in the subsequentKalman filter estimations. This latter filter is to estimate the state variables, subject to both dynamic andstatic equality constraints. Using accumulative balancing constraints, a method is proposed to detect andisolate the existence of single gross error in a dynamic system. A simulated example is used to illustrate theuse and performance of this proposed dynamic data reconciliation method.
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