The Experts below are selected from a list of 25374 Experts worldwide ranked by ideXlab platform
Jose C Principe - One of the best experts on this subject based on the ideXlab platform.
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maximum correntropy kalman filter
Automatica, 2017Co-Authors: Badong Chen, Xi Liu, Haiquan Zhao, Jose C PrincipeAbstract:Abstract Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian Assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean vector and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is also given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.
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maximum correntropy kalman filter
arXiv: Machine Learning, 2015Co-Authors: Badong Chen, Xi Liu, Haiquan Zhao, Jose C PrincipeAbstract:Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian Assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.
Badong Chen - One of the best experts on this subject based on the ideXlab platform.
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robust proportionate adaptive filter based on maximum correntropy criterion for sparse system identification in impulsive noise environments
Signal Image and Video Processing, 2018Co-Authors: Dongqiao Zheng, Zhiyu Zhang, Jiandong Duan, Badong ChenAbstract:Proportionate-type adaptive filtering (PtAF) algorithms have been successfully applied to sparse system identification. The major drawback of the traditional PtAF algorithms based on the mean square error (MSE) criterion show poor robustness in the presence of impulsive noises or abrupt changes because MSE is only valid and rational under Gaussian Assumption. However, this Assumption is not satisfied in most real-world applications. To improve its robustness under non-Gaussian environments, we incorporate the maximum correntropy criterion (MCC) into the update equation of the PtAF to develop proportionate MCC (PMCC) algorithm. The mean and mean square convergence performance analysis are also performed. Simulation results in sparse system identification and echo cancellation applications are presented, which demonstrate that the proposed PMCC exhibits outstanding performance under the impulsive noise environments.
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maximum correntropy kalman filter
Automatica, 2017Co-Authors: Badong Chen, Xi Liu, Haiquan Zhao, Jose C PrincipeAbstract:Abstract Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian Assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean vector and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is also given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.
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maximum correntropy kalman filter
arXiv: Machine Learning, 2015Co-Authors: Badong Chen, Xi Liu, Haiquan Zhao, Jose C PrincipeAbstract:Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian Assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.
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maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non Gaussian environments
Journal of The Franklin Institute-engineering and Applied Mathematics, 2015Co-Authors: Guan Gui, Jihong Zhao, Badong ChenAbstract:Abstract Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS),which are robust under Gaussian Assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.
Yi Cao - One of the best experts on this subject based on the ideXlab platform.
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nonlinear process fault detection and identification using kernel pca and kernel density estimation
Systems Science & Control Engineering, 2016Co-Authors: Raphael T Samuel, Yi CaoAbstract:ABSTRACTKernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA–KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA–KDE is more robust and provide better overall performance than KPCA with Gaussian Assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed.
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nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations
IEEE Transactions on Industrial Informatics, 2010Co-Authors: Pabara-ebiere Odiowei, Yi CaoAbstract:The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two commonly used techniques for process monitoring. Both PCA and PLS assume that the data to be analysed are not self-correlated i.e. time-independent. However, most industrial processes are dynamic so that the Assumption of time-independence made by the PCA and the PLS is invalid in nature. Dynamic extensions to PCA and PLS, so called DPCA and DPLS, have been developed to address this problem, however, unsatisfactorily. Nevertheless, the Canonical Variate Analysis (CVA) is a state-space-based monitoring tool, hence is more suitable for dynamic monitoring than DPCA and DPLS. The CVA is a linear tool and traditionally for simplicity, the upper control limit (UCL) of monitoring metrics associated with the CVA is derived based on a Gaussian Assumption. However, most industrial processes are nonlinear and the Gaussian Assumption is invalid for such processes so that CVA with a UCL based on this Assumption may not be able to correctly identify underlying faults. In this work, a new monitoring technique using the CVA with UCLs derived from the estimated probability density function through kernel density estimations (KDEs) is proposed and applied to the simulated nonlinear Tennessee Eastman Process Plant. The proposed CVA with KDE approach is able to significantly improve the monitoring performance and detect faults earlier when compared to other methods also examined in this study.
Jawad A Salehi - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of time hopping spread spectrum multiple access systems uncoded and coded schemes
IEEE Transactions on Wireless Communications, 2002Co-Authors: Amir R Forouzan, Masoumeh Nasirikenari, Jawad A SalehiAbstract:An ultra-wide bandwidth time-hopping spread-spectrum code division multiple-access system employing a binary PPM signaling has been introduced by Scholtz (1993), and its performance was obtained based on a Gaussian distribution Assumption for the multiple-access interference. In this paper, we begin first by proposing to use a practical low-rate error correcting code in the system without any further required bandwidth expansion. We then present a more precise performance analysis of the system for both coded and uncoded schemes. Our analysis shows that the Gaussian Assumption is not accurate for predicting bit error rates at high data transmission rates for the uncoded scheme. Furthermore, it indicates that the proposed coded scheme outperforms the uncoded scheme significantly, or more importantly, at a given bit error rate, the coding scheme increases the number of users by a factor which is logarithmic in the number of pulses used in time-hopping spread-spectrum systems.
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performance analysis of ultrawideband time hopping code division multiple access systems uncoded and coded schemes
International Conference on Communications, 2001Co-Authors: Amir R Forouzan, Masoumeh Nasirikenari, Jawad A SalehiAbstract:In a previous paper by R.A. Scholtz (see Proc. IEEE MILCOM '93, p.447-50, Oct. 1993), an ultrawide bandwidth time-hopping spread-spectrum code division multiple access system employing a binary PPM signaling was introduced, and its performance was obtained based on a Gaussian distribution Assumption for the multiple access interference. A coded scheme of this system was proposed by the authors (see Proc. PIMRC '00, vol.2, p.1555-8, Sept. 2000), which showed much better performance in comparison to the uncoded scheme under the above Gaussian Assumption. In this paper, we present a more precise performance analysis of the system for both coded and uncoded schemes. Our analysis shows that the Gaussian Assumption is not accurate for predicting bit error rates at high data transmission rates.
Haiquan Zhao - One of the best experts on this subject based on the ideXlab platform.
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polynomial variable scaling factor improved least sum of exponentials algorithm with maximum correntropy criterion
IFAC-PapersOnLine, 2019Co-Authors: Zhengyan Luo, Haiquan ZhaoAbstract:Abstract In this paper, a polynomial variable scaling factor improved least sum of exponentials algorithm with maximum correntropy criterion is proposed for sparse system identification. Sparse system estimation problem is increasing important topics in broadband wireless communications systems. Sparse learning algorithms for system identification achieved a better performance under Gaussian Assumption, such as the zero-attracting least mean square (ZA-LMS). However, in non-Gaussian environments the existing algorithms suffer from performance degradation due to random impulsive noises. To further improve the robustness of the zero-attracting algorithms, an attempt has been made to design an improved sum of error exponentials that utilize the maximum correntropy criterion. In addition, a polynomial zero attractor is introduced to enhance the capability of sparse system identification. The test on sparse system identifications under an impulsive noise environment demonstrates that the proposed algorithm has a low steady-state misalignment compared with the others.
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maximum correntropy kalman filter
Automatica, 2017Co-Authors: Badong Chen, Xi Liu, Haiquan Zhao, Jose C PrincipeAbstract:Abstract Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian Assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean vector and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is also given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.
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maximum correntropy kalman filter
arXiv: Machine Learning, 2015Co-Authors: Badong Chen, Xi Liu, Haiquan Zhao, Jose C PrincipeAbstract:Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian Assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.