期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 24, 期 2-7, 页码 445-451出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0098-1354(00)00436-1
关键词
rectification; Bayesian; wavelets; error-in-variables; Kalman filter
A multiscale approach to data rectification is proposed for data containing features with different time and frequency localization. Noisy data are decomposed into contributions at multiple scales and a Bayesian optimization problem is solved to rectify the wavelet coefficients at each scale. A linear dynamic model is used to constrain the optimization problem, which facilitates an error-in-variables (EIV) formulation and reconciles all measured variables. Time-scale recursive algorithms are obtained by propagating the prior with temporal and scale models. The multiscale Kalman filter is a special case of the proposed Bayesian EIV approach. (C) 2000 Elsevier Science Ltd. All rights reserved.
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