Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/345739
Type: Artigo
Title: Robust estimation and filtering for poorly known models
Author: Fernandes, Marcos R.
Val, Joao B. R. do
Souto, Rafael F.
Abstract: This letter addresses the estimation and filtering problems of systems when only a rough model is available. Based on a modified version of the classic regularized least square problem, a new design criterion for estimation is proposed that considers measurements and innovations as a possible source of uncertainty. Under Gaussian assumption, it performs as an upper bound for the maximum a posteriori Bayesian estimator. The optimal solution is obtained by exploiting non-smooth analysis tools and the optimal solution reveals a region in the residue space for which the non-variation of the estimate is optimal. The approach provides robust estimators from a stochastic point of view in recursive form. To illustrate, a Kalman-like filter is derived and comparison with classic worst-case robust design filters are made
Subject: Sistemas incertos
Filtragem de Kalman
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/lcsys.2019.2951611
Address: https://ieeexplore.ieee.org/document/8891731
Date Issue: 2020
Appears in Collections:FEEC - Artigos e Outros Documentos

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