相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Engineering, Multidisciplinary
Peng Yun et al.
Summary: This paper proposes a variational Bayesian-based cubature Kalman filter to solve the state estimation problem of nonlinear discrete-time systems under dynamic model mismatch and outliers interference. By dividing the measurement noise into normal measurement noise and outlier noise, and using two inverse Wishart distributions to model the unknown covariance of the normal measurement noise and outlier noise, the proposed filter can effectively handle the effects of dynamic model mismatch and outliers interference.
Article
Engineering, Multidisciplinary
Xuhang Liu et al.
Summary: This paper proposes a robust variational Bayesian method-based SINS/GPS integrated system to overcome the influence of non-Gaussian noise and unknown measurement noise. The method estimates the unknown measurement noise covariance using a variational Bayesian-based Kalman filter, and handles interference from non-Gaussian noise using the maximum correntropy criterion. Additionally, the robust variational Bayesian method, based on the interacting multiple model, is designed to avoid interference from non-Gaussian noise to the estimation result of measurement noise covariance, and its robustness and adaptivity are verified through numerical simulation.
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)
Letter
Engineering, Aerospace
Mingming Bai et al.
Summary: This article introduces a robust Kalman filter based on generalized t distribution, suitable for state-space models affected by state and measurement outliers. By directly modeling the state transition and measurement likelihood densities as generalized t distributions, an analytical closed-form solution is obtained through variational inference.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Haoshen Lin et al.
Summary: This paper investigates a noise covariance adaptive distributed Bayesian filter based on variational Bayesian inference method, approximating the joint posterior distribution of state and noises by recursively performing variational Bayesian expectation and maximization steps. It proposes a variational Bayesian based distributed adaptive cubature information filter to approximate Gaussian interval for effective estimation in cooperative object tracking problem.
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
Mingming Bai et al.
Summary: A novel heavy-tailed mixture (HTM) distribution and a robust Kalman filter based on this distribution are proposed in this article. The effectiveness of the proposed filter is verified through a lake experiment, showing a significant improvement in cooperative localization accuracy for AUVs.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Multidisciplinary
Zilu He et al.
Summary: The study established geomagnetic and infrared attitude measurement models to improve reliability, designed the IMMCKF algorithm to enhance system adaptability, and verified its high accuracy and strong anti-interference through experiments, showing significant superiority over CKF and IMMEKF.
Article
Robotics
Seyed Fakoorian et al.
Summary: This work presents a resilient and adaptive state estimation framework, AMCCKF, for robots operating in perceptually-degraded environments, which is able to robustly handle corrupted measurements and adjust filter parameters online for improved performance. Two methods are developed, modifying noise models and kernel bandwidth based on measurement quality, with differences in computational complexity and overall performance. The framework is validated through real experiments on aerial and ground robots, forming part of the solution used in the DARPA Subterranean Challenge by the COSTAR team.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Hongwei Wang et al.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2020)
Article
Engineering, Electrical & Electronic
Shengxin Li et al.
IEEE SENSORS JOURNAL
(2020)
Article
Computer Science, Information Systems
Jingjing He et al.
Article
Automation & Control Systems
Seyed Fakoorian et al.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2019)
Article
Automation & Control Systems
Yulong Huang et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2018)
Article
Physics, Multidisciplinary
Bowen Hou et al.
Article
Engineering, Aerospace
Yulong Huang et al.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2017)
Article
Automation & Control Systems
Badong Chen et al.
Article
Automation & Control Systems
Ienkaran Arasaratnam et al.
Article
Automation & Control Systems
Simo Sarkka et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2009)
