Multi-Kernel Maximum Correntropy Kalman Filter

Li, Shilei; Shi, Dawei; Zou, Wulin; Shi, Ling

doi:10.1109/lcsys.2021.3114137

Cited by 15 publications

(3 citation statements)

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“…(6) Then, the predicted state estimation and state error covariance are obtained as ( 26), ( 27). ( 7) Calculating the predicted measurement in (31), measurement errors in (32) and the gain of the SVSF in (33). ( 8) Comparing the constant and time-varying layer, in the event that the fixed boundary layer's width surpasses the width of its time-varying counterpart, the SR-CKF based on the MKMC is used to refine the precision of the filter.…”

Section: The Csvsf Based On Multi-kernel Maximum Correntropy Criterionmentioning

confidence: 99%

“…θ refers to the velocity of the vertical movement and the velocity of the pitch angle in an earth-fixed frame, w q holds an equivalent interpretation in the body-fixed coordinate, and U e is the steady speed of the FSHC. The means of the variable of ( 63), (64) can refer to the [31,32]. The nonlinear second-order state space function of the FSHC is designed as:…”

Section: Examplementioning

confidence: 99%

“…Specifically, the maximum correntropy criterion (MCC) has shown effectiveness in non-Gaussian conditions by maximizing the correntropy between the system output and desired performance [26,27]. Kalman filtering methods based on the MCC have exhibited exceptional performance in impulsive noise scenarios [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combined Cubature Kalman and Smooth Variable Structure Filtering Based on Multi-Kernel Maximum Correntropy Criterion for the Fully Submerged Hydrofoil Craft

Niu,

Liu

2024

Applied Sciences

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This paper introduces a novel filter algorithm termed as an MKMC-CSVSF which combined square-root cubature Kalman (SR-CKF) and smooth variable structure filtering (SVSF) under multi-kernel maximum correntropy criterion (MKMC) for accurately estimating the state of the fully submerged hydrofoil craft (FSHC) under the influence of uncertainties and multivariate heavy-tailed non-Gaussian noises. By leveraging the precision of the SR-CKF and the robustness of the SVSF against system uncertainties, the MKMC-CSVSF integrates these two methods by introducing a time-varying smooth boundary layer along with multiple fading factors. Furthermore, the MKMC is introduced for the adjustment of kernel bandwidths across different channels to align with the specific noise characteristics of each channel. A fuzzy rule is devised to identify the appropriate kernel bandwidths to ensure filter accuracy without undue complexity. The precision and robustness of state estimation in the face of heavy-tailed non-Gaussian noises are improved by modifying the SR-CKF and the SVSF using a fixed-point approach based on the MKMC. The experimental results validate the efficacy of this algorithm.

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Section: The Csvsf Based On Multi-kernel Maximum Correntropy Criterionmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%