A novel fusion maximum correntropy Kalman/UFIR filter for state estimation with uncertain non-Gaussian noise statistics

被引：10

作者：

Liu, Zheng ^{[1
]}

Zhang, Min ^{[1
]}

Song, Xinmin ^{[1
]}

Yan, Xuehua ^{[2
]}

机构：

[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China

[2] Univ Jinan, Sch Elect Engn, Jinan 250022, Peoples R China

来源：

MEASUREMENT | 2023年 / 220卷

基金：

中国国家自然科学基金;

关键词：

Maximum correntropy Kalman filter; Unbiased finite impulse response filter; Fusion filter; State estimation; Non-Gaussian noise; IGNORING NOISE; H-INFINITY;

D O I：

10.1016/j.measurement.2023.113339

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Despite the higher estimation accuracy of the maximum correntropy Kalman filter (MCKF) algorithm in system with non-Gaussian noise, its estimation performance will decrease when the noise source changes due to disturbances from external uncertainties. Fortunately, the unbiased finite impulse response predictor (UFIR) can overcome the MCKF problems by automatically ignoring the noise related to both the process and measurements. Meanwhile, the MCKF could resolve the UFIR problems, such as non-optimal estimation performance and risk of dead zones. Therefore, a novel fusion maximum correntropy Kalman/UFIR filter is proposed to achieve a more advantageous estimation effect, in which specific probability weights are assigned to the two filters to implement the fusion of two filters. The simulation results demonstrate the superiority of the presented fusion filter algorithm when compared to the fusion Kalman/UFIR filter regarding the estimation performance under the non-Gaussian noise system.

引用

页数：7

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