A Robust Gaussian Approximate Fixed-Interval Smoother for Nonlinear Systems With Heavy-Tailed Process and Measurement Noises

被引：97
|
作者：
Huang, Yulong [1 ]
Zhang, Yonggang [1 ]
Li, Ning [1 ]
Chambers, Jonathon [2 ]
机构：
[1] Harbin Engn Univ, Dept Automat, Harbin 150001, Peoples R China
[2] Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
来源：
IEEE SIGNAL PROCESSING LETTERS | 2016年 / 23卷 / 04期
基金：
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
Gaussian approximate (GA) smoother; heavy-tailed noise; Student's t distribution; variational Bayesian (VB);
D O I：
10.1109/LSP.2016.2533543
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this letter, a robust Gaussian approximate (GA) fixed-interval smoother for nonlinear systems with heavy-tailed process and measurement noises is proposed. The process and measurement noises are modeled as stationary Student's t distributions, and the state trajectory and noise parameters are inferred approximately based on the variational Bayesian (VB) approach. Simulation results show the efficiency and superiority of the proposed smoother as compared with existing smoothers.
引用
收藏
页码：468 / 472
页数：5
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