Robust cubature Kalman filter based on variational Bayesian and transformed posterior sigma points error

被引：30
作者：
Cui, Bingbo [1 ]
Wei, Xinhua [1 ]
Chen, Xiyuan [2 ]
Li, Jinyang [1 ]
Wang, Aichen [1 ]
机构：
[1] Jiangsu Univ, Minist Educ & Jiangsu Prov, Key Lab Modern Agr Equipment & Technol, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Microinertial Instrument & Adv Nav Techno, Nanjing 210069, Jiangsu, Peoples R China
来源：
ISA TRANSACTIONS | 2019年 / 86卷
基金：
中国国家自然科学基金;
关键词：
Robust cubature Kalman filter; Sigma-point update; Variational Bayesian; Integrated navigation; STATE-SPACE MODELS; PERFORMANCE EVALUATION; MEASUREMENT NOISE; ALGORITHM; GNSS/INS;
D O I：
10.1016/j.isatra.2018.11.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An improved robust cubature Kalman filter (RCKF) based on variational Bayesian (VB) and transformed posterior sigma points error is proposed in this paper, which not only retains the robustness of RCKF, but also exhibits adaptivity in the presence of time-varying noise. First, a novel sigma-point update framework with uncertainties reduction is developed by employing the transformed posterior sigma points error. Then the VB is used to estimate the time-varying measurement noise, where the state dependent noise is addressed in the iteratively parameter estimation. The new filter not only reduces the uncertainty on sigma points generation but also accelerates the convergence of VB-based noise estimation. The effectiveness of the proposed filter is verified on integrated navigation, and numerical simulations demonstrate that VB-RCKF outperforms VB-CKF and RCKF. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：18 / 28
页数：11
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