Switching Gaussian-heavy-tailed distribution based robust Gaussian approximate filter for INS/GNSS integration

• Published in: Journal of the Franklin Institute 2022-11, Vol.359 (16), p.9271-9295
• Main Authors: Fu, Hongpo, Cheng, Yongmei
• Format: Article
• Language: English
• Subjects: Gaussian approximate filter; INS/GNSS integration; Non-stationary heavy-tailed noise; Variational Bayesian
• ISSN: 0016-0032; 1879-2693
• DOI: 10.1016/j.jfranklin.2022.08.057

In inertial navigation system and global navigation satellite system (INS/GNSS) integration, the practical stochastic measurement noise may be non-stationary heavy-tailed distribution due to outlier measurements induced by multipath and/or non-line-of-sight receptions of the original GNSS signals. To address the problem, a new switching Gaussian-heavy-tailed (SGHT) distribution is presented, which models the measurement noise with the help of switching between the Gaussian and the an existing heavy-tailed distribution. Then, utilizing two auxiliary parameters satisfying categorical and Bernoulli distributions respectively, we construct the SGHT distribution as a hierarchical Gaussian presentation. Furthermore, applying variational Bayesian inference, a novel SGHT distribution based robust Gaussian approximate filter is derived. Meanwhile, to reduce the computational complexity of the filtering process, an improved fixed-point iteration method is designed. Finally, the simulation of integrated navigation for an aircraft illustrates effectiveness and superiority of the proposed filter as compared the existing robust filters.