UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise

Deng, Zhihong; Lei, Shi; Yin, Lijian; Xia, Yuanqing; Huo, Bo

doi:10.1109/jsen.2020.2980354

Cited by 35 publications

(4 citation statements)

References 27 publications

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“…The unscented Kalman filter (UKF) was suggested to surmount the limitations of EKF. The UKF performs a gaussian approximation of the probability density function using the unscented transformation, which has a superior filtering precision than the EKF [4]. Unfortunately, the accuracy of UKF was excessively depended on parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Strong stabilized joint probabilistic data association method of autoregulatory gating in vehicle nonlinear multi-target tracking

Han,

Liu,

Deng

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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This paper proposed a strong stabilized joint probabilistic data association approach based on numerical stability optimization and autoregulatory gating strategy for multi-target tracking of vehicles with multi-maneuvering states in a nonlinear cluttered environment. The approach utilized an enhanced square-root cubature Kalman filter to achieve the optimization for joint probabilistic data association, which advanced the accuracy and stability of nonlinear state estimation during the multi-target tracking. On this basis, an autoregulation method of association gate based on tracking error sensitivity was proposed to improve the data association gating strategy, which effectively advanced the success rate of target association and tracking accuracy. The virtual simulation traffic scenes of intelligent driving were constructed by PreScan to better reproduce the multi-target tracking problem. The results of the simulations show that the method has clear advantages in terms of target tracking precision, numerical stability and environmental adaptability.

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Section: Introductionmentioning

confidence: 99%

Strong stabilized joint probabilistic data association method of autoregulatory gating in vehicle nonlinear multi-target tracking

Han,

Liu,

Deng

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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“…Correntropy is defined as a nonlinear similarity criterion between two random variables (Li et al, 2020). The correntropy criterion shows acceptable robustness against non-Gaussian noise exposure (Deng et al, 2020). Generalized correntropy (GC) is a generalization of correntropy, in which the generalized Gaussian density (GGD) function replaces the Gaussian kernel in the correntropy (Chen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Insulin infusion rate control using information theoretic–based nonlinear model predictive control for type 1 diabetes patients

Birjandi

Sani

Pariz

2022

Transactions of the Institute of Measurement and Control

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It has been proven that model predictive control (MPC) is an efficient method for closed-loop insulin delivery in clinical studies. This paper aims to design an observer-based fractional-order nonlinear MPC for type 1 diabetes mellitus (T1DM) patients. It is assumed that the proposed model is nonlinear and contains parametric uncertainty. To estimate unknown states, optimal non-fragile H∞ observer is designed for Lipschitz nonlinear fractional-order systems including parametric uncertainty and the existence of input disturbance. The min–max optimization-based robust fractional model predictive control (RFMPC) has been presented in the following for insulin delivery. Since sensor noise of continuous monitoring of interstitial glucose concentration is considered non-Gaussian, the performance of the proposed controller is improved under non-Gaussian measurement noise by selecting a proper cost function based on generalized correntropy, and as a contrast, the performance of the mean square error (MSE)-based controller is simulated. According to the results, not only is the performance of the proposed controller better under non-Gaussian situations but also effectively reaches the set point in the case of disturbance and uncertainty and provides higher control accuracy and robustness compared with the MSE-based MPC.

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“…In recent years, the maximum correntropy Kalman filter (MCKF) was proposed by Chen et al [19], according to the information theoretic learning criterion. Compared with the traditional robust Kalman filter, the MCKF has a higher accuracy and has the ability to deal with various NGNs [20]. Owing to its excellent performance, the MCKF is rapidly applied to various systems [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

Liao

Hirota

et al. 2022

Remote Sensing

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To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the WSNs are used to adjust the noise covariance matrices, and the maximum correntropy criterion is the criterion for the filter’s optimality. By dynamically adjusting the noise covariance matrices, the DSTMCKF ensures that the correntropy distribution is accurate in the presence of non-Gaussian noise (NGN), thus improving its ability to handle the NGN. In simulation and real environment positioning experiments, the DSTMCKF is used to compare with the MCKF, variable kernel width–maximum correntropy Kalman filter (VKW-MCKF) and robust minimum error entropy Kalman filter (R-MEEKF). Among the four filters, the DSTMCKF has the highest accuracy, and the error of the DSTMCKF is reduced by 34.5, 42.9 and 40.0%, respectively, compared with the MCKF, VKW-MCKF and R-MEEKF in the real-world environment positioning experiment. The application of the DSTMCKF in WSNs positioning systems improves the stability of the control systems because of the rising positioning accuracy, which makes WSNs positioning systems more widely used in scenarios requiring high stability, such as automatic parking.

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UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise

Cited by 35 publications

References 27 publications

Strong stabilized joint probabilistic data association method of autoregulatory gating in vehicle nonlinear multi-target tracking

Strong stabilized joint probabilistic data association method of autoregulatory gating in vehicle nonlinear multi-target tracking

Insulin infusion rate control using information theoretic–based nonlinear model predictive control for type 1 diabetes patients

A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems

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