Robust Estimation and Filtering for Poorly Known Models
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...
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Published in: | IEEE control systems letters Vol. 4; no. 2; pp. 474 - 479 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
IEEE
01-04-2020
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Subjects: | |
Online Access: | Get full text |
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Summary: | 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. |
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ISSN: | 2475-1456 2475-1456 |
DOI: | 10.1109/LCSYS.2019.2951611 |