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- W4287147183 abstract "The extended Kalman filter (EKF) is the most popular tool for state estimation of nonlinear stochastic systems. The conventional EKF can be refined via iterative re-linearization, leading to the so-called iterative EKF (IEKF). The performance of either EKF or IEKF will deteriorate in the presence of outliers. In this article, an outlier-resilient IEKF method is proposed using a more robust cost function, the correntropy, to replace the traditional mean squared error criterion. An iterative procedure is derived to maximize the correntropy criterion in a similar way to the Gauss–Newton optimization. Simulation results demonstrate the superiority of the proposed method as compared to the existing methods." @default.
- W4287147183 created "2022-07-25" @default.
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- W4287147183 date "2022-07-25" @default.
- W4287147183 modified "2023-09-26" @default.
- W4287147183 title "Outlier-resilient iterative-extended Kalman filter based on maximum correntropy criterion" @default.
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- W4287147183 doi "https://doi.org/10.1177/01423312221105935" @default.
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