Maximum Correntropy Kalman Filter With State Constraints

Liu, Xi; Chen, Badong; Zhao, Haiquan; Qin, Jing; Cao, Jiuwen

doi:10.1109/access.2017.2769965

Cited by 56 publications

(37 citation statements)

References 30 publications

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“…The popular loss function of standard AE is MSE, but it leads to bad performance in feature learning of the complex and high-dimensional signals [29]- [31]. Correntropy is good at measuring the differences between the predicted distribution and the actual distribution of high-dimensional data [32], [33] and is not sensitive to the non-stationary background noises during high-power disk laser welding.…”

Section: Proposed Methods For Welding Statuses Monitoring a Theomentioning

confidence: 99%

A Low-Cost Welding Status Monitoring Framework for High-Power Disk Laser Welding (December 2018)

et al. 2019

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Welding status determines the post-weld quality and is crucial to high-power disk laser welding. A low-cost monitoring system based on two photodiodes is developed to monitor the real-time welding statuses in this paper. A deep learning architecture based on stacked autoencoder (SAE) is proposed to automatically learn more representative features of welding statuses from the raw signal features captured by the visible light photodiode and the reflected laser light photodiode without any manual operations. The maximum correntropy loss function is applied to improve the learning ability of the proposed SAE method. Furthermore, a genetic algorithm is applied to optimize the key parameters of the proposed SAE method. The proposed SAE is applied to the high-power disk laser welding experiments and shows better performance in welding status monitoring than the standard AE framework and the conventional SVM and BP method. Additional experiments with different welding parameters validate the effectiveness and robustness of our proposed SAE method. INDEX TERMS Autoencoder, genetic algorithm, laser welding, low-cost, welding status.

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Section: Proposed Methods For Welding Statuses Monitoring a Theomentioning

confidence: 99%

A Low-Cost Welding Status Monitoring Framework for High-Power Disk Laser Welding (December 2018)

et al. 2019

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“…The above methods for distributed state estimation are founded based on MMSE criterion, which cannot cope with state evaluation under non-Gaussian noise. In recent years, MCC based Kalman filtering [21], [22] is derived to deal with non-Gaussian noise for linear systems. Correntropy which is sensitive to pulse can be used to survey the local similarity of measurements.…”

Section: Introductionmentioning

confidence: 99%

Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks

Zhang

Gao

et al. 2020

IEEE Access

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In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms. INDEX TERMS Robust cubature information filtering, maximum correntropy criterion, Non-Gaussian measurement noise, distributed state estimation, weighted average consensus.

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“…Recently, information theoretic learning has been gaining more attention for its effectiveness in robust state estimation [14]- [17]. By modifying the optimization criterion using information theoretic quantities (e.g., entropy), high-order statistics of data can be captured.…”

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Square-Root Cubature Kalman Filter for Non-Gaussian Measurement Noise

Sun

Zhang

et al. 2020

IEEE Access

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Cubature Kalman filter (CKF) is widely used for non-linear state estimation under Gaussian noise. However, the estimation performance may degrade greatly in presence of heavy-tailed measurement noise. Recently, maximum correntropy square-root cubature Kalman filter (MCSCKF) has been proposed to enhance the robustness against measurement outliers. As is generally known, the square-root algorithms have the benefit of low computational complexity and guaranteed positive semi-definiteness of the state covariances. Therefore, MCSCKF not only possesses the advantages of square-root cubature Kalman filter (SCKF), but also is robust against the heavy-tailed measurement noise. Nevertheless, MCSCKF is prone to the numerical problems. In this paper, we propose a new maximum correntropy square-root cubature Kalman filter (NMCSCKF) based on a cost function which is obtained by a combination of weighted least squares (WLS) to handle the Gaussian process noise and maximum correntropy criterion (MCC) to handle the heavy-tailed measurement noise. Compared to MCSCKF, the proposed method is more time-efficient and most importantly, it avoids the numerical problem. A univariate non-stationary growth model and a multi-rate vision/IMU integrated attitude measurement model are used to demonstrate the superior performance of the proposed method. INDEX TERMS Square-root cubature Kalman filter, maximum correntropy criterion, vision/IMU integrated measurement.

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Maximum Correntropy Kalman Filter With State Constraints

Cited by 56 publications

References 30 publications

A Low-Cost Welding Status Monitoring Framework for High-Power Disk Laser Welding (December 2018)

A Low-Cost Welding Status Monitoring Framework for High-Power Disk Laser Welding (December 2018)

Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks

Maximum Correntropy Square-Root Cubature Kalman Filter for Non-Gaussian Measurement Noise

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