Switching Gaussian-heavy-tailed distribution based robust Gaussian approximate filter for INS/GNSS integration. Issue 16 (November 2022)

• Record Type: Journal Article
• Authors: Fu, Hongpo; Cheng, Yongmei
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 16(2022)
• Page Start: 9271
• Page End: 9295
• Publication Date: 2022-11
• Subjects: INS/GNSS integration -- Variational Bayesian -- Non-stationary heavy-tailed noise -- Gaussian approximate filter; Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2022.08.057
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: 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.