Minimum Error Entropy Kalman Filter

Chen, Badong; Dang, Lujuan; Gu, Yuantao; Zheng, Nanning; Prı́ncipe, José C.

doi:10.1109/tsmc.2019.2957269

Cited by 128 publications

(41 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We should emphasize that the presented results do not imply that EnRDA always performs better than PF and EnKF. The EnKF at the limiting case M → ∞, in the absence of bias, is a minimum mean squared error estimator and attains the lowest possible posterior variance for linear systems, also referred to as the Cramér-Rao lower bound (Cramér, 1999;Rao et al, 1973). Thus, when the errors are drawn from zero mean Gaussian distributions with a linear observation operator, EnKF can outperform EnRDA in terms of the mean squared error.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Ensemble Riemannian data assimilation over the Wasserstein space

Tamang

Ebtehaj

Leeuwen

et al. 2021

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the translation and difference between the shapes of square-integrable probability distributions of the background state and observations. This enables us to formally penalize geophysical biases in state space with non-Gaussian distributions. The new approach is applied to dissipative and chaotic evolutionary dynamics, and its potential advantages and limitations are highlighted compared to the classic ensemble data assimilation approaches under systematic errors.

show abstract

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Ensemble Riemannian data assimilation over the Wasserstein space

Tamang

Ebtehaj

Leeuwen

et al. 2021

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

show abstract

“…The kernel function in entropy is usually a Gaussian kernel due to its smoothness and strict positive determination. These properties showed the effectiveness of maximum correntropy criterion (MCC) for occlusion and corruption problems [28]- [32], [46], [47]. In particular, MCC is suited for dealing with impulsive noises.…”

Section: Introductionmentioning

confidence: 94%

Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks

Shi

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper focuses on the problem of distributed adaptive estimation over dynamic multi-task networks, where a set of nodes is required to collectively estimate some parameters of interest from noisy measurements. Besides, since nodes in the network are constrained by communication power consumption and external interference in a non-stationary environment, the objective pursued by the node is prone to change or abnormality. The problem is worth considering in several contexts including multi-target tracking, multi-model classification and heterogeneous network segmentation. We propose a distributed adaptive clustering strategy, which is mainly composed of two procedures: normal task adaptation and the same task cluster. The task anomaly detection based on non-cooperative least-mean-squares (NC-LMS) algorithm and task switching detection based on diffusion maximum correntropy criterion (D-MCC) algorithm are provided. A series of scenarios, such as dynamic network, time-varying tasks and non-stationary (Gaussian and pulse interference) are simulated. We also discuss optimization schemes to design the NC-LMS and D-MCC weights and examine the estimate performance and clustering effects of the proposed algorithm by simulation results. INDEX TERMS Adaptive clustering, distributed estimation, multi-task, maximum correntropy criterion.

show abstract

“…Other strategies can be classified as entropy-based, distributionbased, or sliding mode type. Entropy-based strategies utilize a different criterion than the KF, which minimizes the wellknown mean square error (MSE) [14,15]. Both methods attempt to improve robustness to heavy-tailed non-Gaussian noises in order to provide a stable estimate.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Formulation of the Sliding Innovation Filter

Lee

Gadsden

Al-Shabi

2021

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

In this paper, an adaptive formulation of the sliding innovation filter (SIF) is presented. The SIF is a recently proposed estimation strategy that has demonstrated robustness to modeling errors and uncertainties. It utilizes a switching gain that is a function of the innovation (measurement error) and sliding boundary layer term. In this paper, a time-varying sliding boundary layer is derived based on minimizing the state error covariance. The resulting solution creates an adaptive formulation of the SIF. The adaptive SIF is applied on a linear aerospace system, and is compared with the well-known Kalman filter (KF) and the standard SIF. The results demonstrate the robustness of the new estimation strategy in the presence of modeling uncertainties and system faults.

show abstract

Minimum Error Entropy Kalman Filter

Cited by 128 publications

References 46 publications

Ensemble Riemannian data assimilation over the Wasserstein space

Ensemble Riemannian data assimilation over the Wasserstein space

Distributed Adaptive Clustering Based on Maximum Correntropy Criterion Over Dynamic Multi-Task Networks

An Adaptive Formulation of the Sliding Innovation Filter

Contact Info

Product

Resources

About