Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs

Liu, Xinghua; Guan, Jianwei; Jiang, Rui; Gao, Xiang; Chen, Badong; Ge, Shuzhi Sam

doi:10.1049/sil2.12098

Cited by 9 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the actual filtering process, the covariance Ck is calculated in Equation (23). In addition, according to [30], after weighing the robustness of the algorithm and the response speed to NGN, the forgetting factor λ is taken as 0.95.…”

Section: Dynamic Self-tuning Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

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

Liao

Hirota

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Dynamic Self-tuning Algorithmmentioning

confidence: 99%

“…However, because H k is not necessarily reversible, the value of Pk|k−1 cannot be directly calculated by Equation (28). Therefore, the expansion coefficient α k is introduced to adjust Pk|k−1 , as shown in Equations ( 29) and (30):…”

Section: Dynamic Self-tuning Algorithmmentioning

confidence: 99%

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

Liao

Hirota

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Nevertheless, multiple iterations can lead to increased covariance of weight and particle degeneracy with local optimization. The UKF employs a scaled unscented transformation process and deterministic sampling technique to approximate the posteriori probability density with higher precision [8,9]. However, the artificial selection of parameters may impact the filtering performance.…”

Section: Introductionmentioning

confidence: 99%

Combined Cubature Kalman and Smooth Variable Structure Filtering Based on Multi-Kernel Maximum Correntropy Criterion for the Fully Submerged Hydrofoil Craft

Niu,

Liu

2024

Applied Sciences

View full text Add to dashboard Cite

This paper introduces a novel filter algorithm termed as an MKMC-CSVSF which combined square-root cubature Kalman (SR-CKF) and smooth variable structure filtering (SVSF) under multi-kernel maximum correntropy criterion (MKMC) for accurately estimating the state of the fully submerged hydrofoil craft (FSHC) under the influence of uncertainties and multivariate heavy-tailed non-Gaussian noises. By leveraging the precision of the SR-CKF and the robustness of the SVSF against system uncertainties, the MKMC-CSVSF integrates these two methods by introducing a time-varying smooth boundary layer along with multiple fading factors. Furthermore, the MKMC is introduced for the adjustment of kernel bandwidths across different channels to align with the specific noise characteristics of each channel. A fuzzy rule is devised to identify the appropriate kernel bandwidths to ensure filter accuracy without undue complexity. The precision and robustness of state estimation in the face of heavy-tailed non-Gaussian noises are improved by modifying the SR-CKF and the SVSF using a fixed-point approach based on the MKMC. The experimental results validate the efficacy of this algorithm.

show abstract

“…Pan et al proposed an adaptive fading factor to address the uncertainty of process noise. Liu et al introduced fading factors in the error covariance matrix and constructed various ST nonlinear filters (Kiani and Ahmadvand, 2022; Liu et al, 2022). ST filters have also been applied to spacecraft attitude estimation (Huang et al, 2016) and maneuvering target tracking (Ge et al, 2011; Li et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

Gu,

Jing,

2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.

show abstract

Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs

Cited by 9 publications

References 40 publications

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

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

Combined Cubature Kalman and Smooth Variable Structure Filtering Based on Multi-Kernel Maximum Correntropy Criterion for the Fully Submerged Hydrofoil Craft

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

Contact Info

Product

Resources

About