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Robust Variational-based Kalman Filter for Outlier Rejection with Correlated Measurements

Li, Haoqing and Medina, Daniel and Vilà-Valls, Jordi and Closas, Pau Robust Variational-based Kalman Filter for Outlier Rejection with Correlated Measurements. (2021) IEEE Transactions on Signal Processing, 69. 357-369. ISSN 1053-587X

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Official URL: https://doi.org/10.1109/TSP.2020.3042944

Abstract

State estimation is a fundamental task in many engineering fields, and therefore robust nonlinear filtering techniques able to cope with misspecified, uncertain and/or corrupted models must be designed for real-life applicability. In this contribution we explore nonlinear Gaussian filtering problems where measurements may be corrupted by outliers,and propose a new robust variational-based filtering methodology able to detect and mitigate their impact. This method generalizes previous contributions to the case of multiple outlier indicators for both independent and dependent observation models. An illustrative example is provided to support the discussion and show the performance improvement.

Item Type:Article
Additional Information:Thanks to the IEEE (Institute of Electrical and Electronics Engineers). This paper is available at : https://ieeexplore.ieee.org/document/9286419 “© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-03108748
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > German Aerospace Center - DLR (GERMANY)
Other partners > Northeastern University (USA)
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Deposited On:13 Jan 2021 11:36

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