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Maximum Correntropy Criterion Constrained Kalman Filter
Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications, 2017 Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in ...
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Robust variable kernel width for maximum correntropy criterion algorithm
Signal Processing, 2021Abstract Maximum correntropy criterion (MCC) has been widely adopted for parameter estimation in the environment of non-Gaussian noise due to its robust characteristics to non-Gaussian noises. However, choosing a proper fixed value of kernel width in MCC algorithm is not an easy task.
Wei Huang, Jinshan Xu, Xin-Wei Yao
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Symmetry, 2018 A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems.
Yingsong Li, Yanyan Wang, Laijun Sun
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Maximum correntropy criterion based regression for multivariate calibration
Chemometrics and Intelligent Laboratory Systems, 2017 The least-squares criterion is widely used in the multivariate calibration models. Rather than using the conventional linear least-squares metric, we employ a nonlinear correntropy-based metric to describe the spectra-concentrate relations and propose a ...
Jiangtao Peng, Kaifeng Rao, Qiwei Xie
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A robust maximum correntropy criterion for dictionary learning
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016We introduce a method that incorporates robustness to one of the main building blocks of sparse modeling: dictionary learning. Particularly, we exploit correntropy to compute the principal components in cases where outliers might be detrimental without proper care.
Carlos A. Loza, José C. Príncipe
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Maximum correntropy criterion for discriminative dictionary learning
2013 IEEE International Conference on Image Processing, 2013In this paper, a novel discriminative dictionary learning with pairwise constraints by maximum correntropy criterion is proposed for pair matching problem. Comparing with the conventional dictionary learning approaches, the proposed method has several advantages: (i) It can deal with the outliers and noises problem more efficiently during the ...
Pengyi Hao, Sei-ichiro Kamata
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A maximum correntropy criterion for robust multidimensional scaling
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015Multidimensional Scaling (MDS) refers to a class of dimensionality reduction techniques applied to pairwise dissimilarities between objects, so that the interpoint distances in the space of reduced dimensions approximate the initial pairwise dissimilarities as closely as possible.
Fotios D. Mandanas +1 more
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Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise
Entropy, 2017 As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model.
Bowen Hou, Zhangming He, Haiyin Zhou
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Extended Kalman filter under maximum correntropy criterion
2016 International Joint Conference on Neural Networks (IJCNN), 2016As a nonlinear extension of Kalman filter, the extended Kalman filter (EKF) is also based on the minimum mean square error (MMSE) criterion. In general, the EKF performs well in Gaussian noises. But its performance may deteriorate substantially when the system is disturbed by heavy-tailed impulsive noises.
Xi Liu 0006 +3 more
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Random Fourier Filters Under Maximum Correntropy Criterion
IEEE Transactions on Circuits and Systems I: Regular Papers, 2018Random Fourier adaptive filters (RFAFs) project the original data into a high-dimensional random Fourier feature space (RFFS) such that the network structure of filters is fixed while achieving similar performance with kernel adaptive filters. The commonly used error criterion in RFAFs is the well-known minimum mean-square error (MMSE) criterion, which
Shiyuan Wang +5 more
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