Open AccessRobust H‐infinity CKF/KF hybrid filtering method for SINS alignment
Author(s) -
Zhang Lei,
Yang Chun,
Chen Qingwei,
Yan Fei
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0133
Subject(s) - kalman filter , robustness (evolution) , control theory (sociology) , outlier , inertial navigation system , filter (signal processing) , computer science , noise (video) , algorithm , mathematics , artificial intelligence , computer vision , biochemistry , chemistry , geometry , orientation (vector space) , image (mathematics) , gene , control (management)
This study concerns the in‐motion alignment in the strapdown inertial navigation system (SINS) with large misalignment angles. As the non‐linear filtering method applied in the alignment model is quite computer intensive, which has a significant impact on the alignment accuracy and speed. To solve this problem, a robust H‐infinity cubature Kalman filter (CKF)/KF hybrid filter (RHCHF) is proposed to lower the computational burden and strengthen the robustness. By virtue of the idea of model decomposition, the RHCHF could estimate the non‐linear and linear parts of alignment model, respectively. Through the introduction of robust factor to adjust the filter parameters, it can ensure the accuracy reliably. The comparisons of the simulation and vehicle experiment demonstrate that the RHCHF could achieve the results at a significantly lower expense than the unscented Kalman filter, and obtain a high accuracy even when the statistical property of noise is uncertain or the outliers of measurement occur occasionally.