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A Sparse adaptive Bayesian filter for input estimation problems

Abstract : The present paper introduces a novel Bayesian filter for estimating mechanical excitation sources in the time domain from a set of vibration measurements. The proposed filter is derived from a very general Bayesian formulation, unifying most of the state-of-the-art recursive filters developed in the last decade for solving input-state estimation problems. More specifically, the proposed Bayesian filter allows promoting the spatial sparsity of the estimated input vector, by assuming that the predicted input vector is a random vector with independent and identically distributed components following a generalized Gaussian distribution. To properly estimate the most probable parameters of the latter probability distribution, a nested Bayesian optimization is implemented. The validity of the proposed approach, called Sparse adaptive Bayesian Filter, is assessed both numerically and experimentally. In particular, the comparisons performed with some state-of-the-art filters show that the proposed strategy outperforms the existing filters in terms of input estimation accuracy and avoids the so-called drift effect.
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Contributor : Mathieu Aucejo Connect in order to contact the contributor
Submitted on : Tuesday, June 21, 2022 - 9:32:28 AM
Last modification on : Monday, October 17, 2022 - 11:45:08 AM
Long-term archiving on: : Thursday, September 22, 2022 - 6:54:45 PM


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Julian Ghibaudo, Mathieu Aucejo, Olivier de Smet. A Sparse adaptive Bayesian filter for input estimation problems. Mechanical Systems and Signal Processing, 2022, ⟨10.1016/j.ymssp.2022.109416⟩. ⟨hal-03700301⟩



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