.png){kind=icon}
返回
Maximum Correntropy Generalized Conversion-Based Nonlinear Filtering
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2024.3461835.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nonlinear filtering methods have gained prominence in various applications, and one of the notable methods is the generalized conversion filter (GCF) based on deterministic sampling. The GCF offers an innovative method for converting measurements, exhibiting superior estimation performance when compared to several popular existing nonlinear estimators. However, a notable limitation of existing GCF is their reliance on the minimum mean square error (MMSE) criterion. While GCF excels in environments with Gaussian noise, their performance can significantly deteriorate in the presence of non-Gaussian noise, particularly when subjected to heavy-tailed impulse noise interference. To address this challenge and enhance the robustness of GCF against impulse noise, this article proposes a novel nonlinear filter known as the maximum correntropy GCF (MCGCF). Similar to GCF, the proposed filter also employs a general measurement conversion, wherein deterministic sampling is utilized to optimize the first and second moments of multidimensional transformations. To obtain a robust posterior estimate of the state and covariance matrices, the MCGCF employs a nonlinear regression method to derive state updates based on the maximum correntropy criterion (MCC). To validate the efficacy of the proposed MCGCF, two experiments are presented. These experiments illustrate the filter's ability to deliver robust and accurate estimates, even in challenging scenarios with nonlinear systems and non-Gaussian noises.
Keyword:
Noise
Kalman filters
Covariance matrices
Nonlinear systems
Time measurement
State estimation
Noise measurement
Deterministic sampling (DS)
generalized conversion filter (GCF)
maximum correntropy criterion (MCC)
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal Volumetry, Tensor-Based Morphometry and Voxel-Based Morphometry.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏分享收藏Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter Method for State-of-Charge Estimation of Li-Ion Battery Cells基于变分贝叶斯的最大相关熵容积卡尔曼滤波法估计锂离子电池的荷电状态
OAAI
OAAI分享收藏Adaptive Extended Kalman Filtering Based State-of-Charge and Voltage Estimation of a Lithium-Ion Battery Using a Fractional-Order Model基于自适应扩展卡尔曼滤波的分数阶锂离子电池荷电状态和电压估计IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Dual-Triggered Distributed Maximum Correntropy Kalman Filter With Measurement Noise Variance Compensation for Sensor NetworksIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏分享收藏
查看更多内容