Robust Student’s t-based Rauch-Tung-Striebel Smoother for Non-stationary Heavy-tailed Measurement Noises

作     者：Fengchi Zhu Chao Xue Yulong Huang Yonggang Zhang 

作者单位：College of Intelligent Systems Science and EngineeringHarbin Engineering University 

会议名称：《第40届中国控制会议》

会议日期：2021年

学科分类：12[管理学] 083002[工学-环境工程] 1204[管理学-公共管理] 120402[管理学-社会医学与卫生事业管理(可授管理学、医学学位)] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 0837[工学-安全科学与工程] 

关 键 词：State estimation Rauch-Tung-Striebel smoother non-stationary heavy-tailed measurement noises variational Bayesian Student’s t-distribution 

摘      要：To address the fixed-interval smoothing problem for a linear state-space model with non-stationary heavy-tailed measurement noises,a robust Student’s t-based Rauch-Tung-Striebel smoother is proposed in this *** measurement noise is modelled as a non-stationary Student’s t-distribution,which is written as a Gaussian hierarchical form by introducing Gaussian and Gamma random *** time-varying state trajectory,Student’s t-distribution parameters and auxiliary random variables are jointly estimated by using the variational Bayesian *** study shows that the proposed smoother outperforms the existing up-to-date smoothers in the face of non-stationary heavy-tailed measurement noises.