Results 141 to 150 of about 935 (185)

Multi-Kernel Maximum Correntropy Kalman Filter

IEEE Control Systems Letters, 2022
Maximum correntropy criterion (MCC) has been widely used in Kalman filter to cope with heavy-tailed measurement noises. However, its performance on mitigating non-Gaussian process noises and unknown disturbance is rarely explored. In this letter, we extend the definition of correntropy from a single kernel to multiple kernels.
Shilei Li, Dawei Shi, Wulin Zou, Ling Shi 0001   +3 more
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Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation [PDF]

Sensors, 2016
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks.
Xi Liu, Pengcheng Yue, Qu Hua
exaly   +3 more sources

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Kernel recursive maximum correntropy

Signal Processing, 2015
In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum correntropy (KRMC), is derived in kernel space and under the maximum correntropy criterion (MCC). The proposed algorithm is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive ...
Wentao Ma, Badong Chen
exaly   +2 more sources

A distributed maximum correntropy Kalman filter

Signal Processing, 2019
Abstract Most distributed Kalman filters are based on the cost function of the well-known minimum mean square estimation criterion, which performs well in the presence of Gaussian noise. When impulsive noise is involved, the performance of distributed Kalman filters may become worse.
Gang Wang, Rui Xue, Jinxin Wang
exaly   +2 more sources

Maximum Correntropy Estimation Is a Smoothed MAP Estimation

IEEE Signal Processing Letters, 2012
As a new measure of similarity, the correntropy can be used as an objective function for many applications. In this letter, we study Bayesian estimation under maximum correntropy (MC) criterion. We show that the MC estimation is, in essence, a smoothed maximum a posteriori (MAP) estimation, including the MAP and the minimum mean square error (MMSE ...
Badong Chen, JOSÉ C Principe
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Kernel Recursive Generalized Maximum Correntropy

IEEE Signal Processing Letters, 2017
In this letter, a novel kernel adaptive algorithm, called kernel recursive generalized maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the generalized maximum correntropy (GMC) criterion. The proposed kernel algorithm can effectively scale down the dynamic recursive weight coefficients influenced by the impulsive estimate ...
Ji Zhao, Hongbin Zhang
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Robust tensor factorization using maximum correntropy criterion

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Traditional tensor decomposition methods, e.g., two dimensional principle component analysis (2DPCA) and two dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE) and are sensitive to outliers. In this paper, we propose a new robust tensor factorization method using maximum correntropy criterion (MCC) to improve the ...
Miaohua Zhang, Yongsheng Gao 0001, Changming Sun, John La Salle, Junli Liang   +4 more
openaire   +2 more sources

Maximum Correntropy Criterion for Robust Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l(1)norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy ...
Ran He, Wei-Shi Zheng, Bao-Gang Hu
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Convex regularized recursive maximum correntropy algorithm

Signal Processing, 2016
In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced ...
Kaixin Li, Yuli Fu, Haiquan Zhao
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Robust Multikernel Maximum Correntropy Filters

IEEE Transactions on Circuits and Systems II: Express Briefs, 2020
The multikernel adaptive filters based on the minimum mean square error (MMSE) criterion have been proposed to improve the performance of the kernel least mean square (KLMS), efficiently. However, these multikernel methods suffer from large computational burden as well as instability in impulsive noises.
Kui Xiong, Wei Shi, Shiyuan Wang
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