Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering

Liu, Xi; Ren, Zhigang; Lyu, Hongqiang; Jiang, Zhihong; Ren, Pengju; Chen, Badong

doi:10.1109/tsmc.2019.2917712

Cited by 90 publications

(33 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Chebyshev functional link expansion in (5) involves 2M e = 2P M i multiplications and M e = P M i additions at each iteration. The Legendre functional link expansion in (7) is very similar to the Chebyshev one, but it requires slightly larger resources.…”

Section: B Computational Analysis Of the Functional Expansionsmentioning

confidence: 99%

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

Comminiello

Nezamdoust

Scardapane

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

Nonlinear models are known to provide excellent performance in real-world applications that often operate in non-ideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear models for online applications. The proposed algorithms are based on linear-in-the-parameters (LIP) nonlinear filters using functional link expansions. In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters. Among this family of algorithms, we also define the partitioned-block frequency-domain FLAF, whose implementation is particularly suitable for online nonlinear modeling problems. We assess and compare frequency-domain FLAFs with different expansions providing the best possible tradeoff between performance and computational complexity. Experimental results prove that the proposed algorithms can be considered as an efficient and effective solution for online applications, such as the acoustic echo cancellation, even in the presence of adverse nonlinear conditions and with limited availability of computational resources.

show abstract

Section: B Computational Analysis Of the Functional Expansionsmentioning

confidence: 99%

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

Comminiello

Nezamdoust

Scardapane

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

show abstract

“…In the following, we begin by expanding each of the components in (24) and then describe the uncertainty term ∆ as a linear function of e plus a bounded term.…”

Section: A Properties Of the Uncertainty Term ∆mentioning

confidence: 99%

“…However, in this paper, the performance of the ordinary EKF may break down for the influenza epidemics of an interactive society that is disturbed by non-Gaussian noise (when the society is not isolated). To solve this issue, the maximum correntropy Kalman filter (MCKF) can be utilized to provide robustness for the Kalman filter in the presence of non-Gaussian noise or large outliers [22]- [24]. The MCKF uses the correntropy criterion instead of MMSE through which higher-order information of process and measurement noises is used [25], [26].…”

Section: Introductionmentioning

confidence: 99%

State estimation-based robust optimal control of influenza epidemics in an interactive human society

Azimi

Sharifi

Fakoorian

et al. 2022

Information Sciences

View full text Add to dashboard Cite

“…The Maximum Correntropy Criterion (MCC) [26] has good effect in evaluating non-Gaussian noise. There are many filters based on MCC that have been proposed [27][28][29][30], which can obtain the error's higher-order moments and filter the outliers effectively. They have made significant progress in solving non-Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Generalized Maximum Correntropy Kalman Filter for Target Tracking in TianGong-2 Space Laboratory

et al. 2022

Self Cite

View full text Add to dashboard Cite

Target tracking plays an important role in the construction, operation, and maintenance of the space station by the robot, which puts forward high requirements on the accuracy of target tracking. However, the special space environment may cause complex non-Gaussian noise in target tracking data. And the performance of traditional Kalman Filter will deteriorate seriously when the error signals are non-Gaussian, which may lead to mission failure. In the paper, a novel Kalman Filter algorithm with Generalized Maximum Correntropy Criterion (GMCKF) is proposed to improve the tracking accuracy with non-Gaussian noise. The GMCKF algorithm, which replaces the default Gaussian kernel with the generalized Gaussian density function as kernel, can adapt to multi-type non-Gaussian noises and evaluate the noise accurately. A parameter automatic selection algorithm is proposed to determine the shape parameter of GMCKF algorithm, which helps the GMCKF algorithm achieve better performance for complex non-Gaussian noise. The performance of the proposed algorithm has been evaluated by simulations and the ground experiments. Then, the algorithm has been applied in the maintenance experiments in TianGong-2 space laboratory of China. The results validated the feasibility of the proposed method with the target tracking precision improved significantly in complex non-Gaussian environment.

show abstract

Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering

Cited by 90 publications

References 44 publications

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

State estimation-based robust optimal control of influenza epidemics in an interactive human society

Generalized Maximum Correntropy Kalman Filter for Target Tracking in TianGong-2 Space Laboratory

Contact Info

Product

Resources

About