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Article
Automation & Control Systems
Hao Zhu et al.
Summary: This article proposes a novel variational Bayesian adaptive Kalman filter that can handle inaccurate noise covariances in the presence of outliers. The state transition and measurement likelihood probability density functions are modeled as Gaussian-Gamma mixture distributions. The VB inference is used to simultaneously perform the state estimation and estimation of noise covariances. Simulations demonstrate the effectiveness of the proposed method in environments with inaccurate noise covariances and outliers.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Hongpo Fu et al.
Summary: This paper investigates the state estimation problems of systems with unknown non-stationary heavy-tailed noises. A new switching Gaussian-heavy-tailed (SGHT) distribution is proposed to model the noises by adaptively learning the switching probability between the Gaussian distribution and the newly designed heavy-tailed distribution. The SGHT distribution is then expressed as a hierarchical Gaussian presentation using two auxiliary variables satisfying the categorical distribution and the Bernoulli distribution. A new SGHT distribution based robust Kalman filter (SGHT-RKF) is derived using variational Bayesian (VB) inference. Simulations are performed to demonstrate the superior performance of the developed filter compared with existing filters.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Engineering, Electrical & Electronic
Baojian Yang et al.
Summary: This paper proposes a centered error entropy based variational Bayesian adaptive and robust Kalman filter (CEEVBKF) to suppress outlier noise and estimate the unknown noise covariance adaptively. It improves the iterative efficiency and reduces the parameter sensitivity by jointly estimating the centered error entropy and variational Bayesian.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Automation & Control Systems
Mingming Bai et al.
Summary: This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. The AORSE is developed by maximizing a hybrid SSMs based cost function, which improves the accuracy of the algorithm. Simulation and experimental examples show the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Mingming Bai et al.
Summary: A robust fixed-interval smoother for nonlinear systems with heavy-tailed state and measurement noises is proposed, where noise distributions are modelled as Gaussian-Student's t mixtures. The merits of this smoother are demonstrated through numerical simulation and target tracking examples.
Article
Automation & Control Systems
Hao Zhu et al.
Summary: This article explores state estimation with unknown non-stationary heavy-tailed process and measurement noises, proposing a model based on mixture of Gaussian distributions and a variational Bayesian algorithm to effectively address filtering tasks under non-stationary HPMN conditions.
Article
Engineering, Electrical & Electronic
Zhuonan Wang et al.
Summary: A novel constrained least mean M-estimation (CLMM) algorithm is proposed in this study, which has lower computational complexity and better steady-state performance compared to previous algorithms for non-Gaussian noise, including noise with multi-peak distribution. The proposed algorithm ensures stability by analyzing mean square stability and outperforms previous algorithms in simulations with non-Gaussian noises.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Automation & Control Systems
Yulong Huang et al.
Summary: This article introduces a statistical similarity measure to quantify the similarity between random vectors, and uses it to develop a new outlier-robust Kalman filtering framework. The approximation errors and stability of the filter are analyzed, and iterative algorithms with convergent conditions are provided. The selection of similarity functions is considered, revealing the relations between the new method and existing outlier-robust Kalman filters. Simulation examples demonstrate the effectiveness and potential of the new filtering scheme.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Badong Chen et al.
Summary: The article introduces a new Kalman-type filter called Minimum Error Entropy KF (MEE-KF), which uses the minimum error entropy criterion instead of MMSE or MCC. Similar to MCC-based KFs, the proposed filter is an online algorithm with a recursive process. Additionally, an MEE extended KF (MEE-EKF) is developed for performance improvement in nonlinear situations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Yulong Huang et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2019)
Article
Automation & Control Systems
Yulong Huang et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2018)
Article
Engineering, Aerospace
Yulong Huang et al.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2017)
Article
Automation & Control Systems
Yulong Huang et al.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2017)
Article
Automation & Control Systems
Badong Chen et al.
Article
Engineering, Electrical & Electronic
S. C. Chan et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2011)
Article
Engineering, Aerospace
Christopher D. Karlgaard et al.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2007)
