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

Bingbo Cui; Xinhua Wei; Xiyuan Chen; Jinyang Li; Aichen Wang

doi:10.1016/j.isatra.2018.11.005

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

ISA Trans. 2019 Mar:86:18-28. doi: 10.1016/j.isatra.2018.11.005. Epub 2018 Nov 9.

Authors

Bingbo Cui¹, Xinhua Wei², Xiyuan Chen³, Jinyang Li², Aichen Wang²

Affiliations

¹ Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang, Jiangsu 212013, China. Electronic address: cuibingbo@163.com.
² Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
³ Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education, Southeast University, Nanjing, Jiangsu 210069, China.

PMID: 30448250
DOI: 10.1016/j.isatra.2018.11.005

Abstract

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.

Keywords: Integrated navigation; Robust cubature Kalman filter; Sigma-point update; Variational Bayesian.