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

DOI10.1016/j.jfranklin.2022.08.057zbMath1501.93154OpenAlexW4296772704MaRDI QIDQ2095009

Hongpo Fu, Yong-Mei Cheng

Publication date: 9 November 2022

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2022.08.057

zbMATH Keywords

navigation systemrobust Gaussian approximate filterswitching Gaussian-heavy-tailed distribution

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10)

Cites Work

• Maximum correntropy Kalman filter
• Robust extended fractional Kalman filter for nonlinear fractional system with missing measurements
• Three-stage unscented Kalman filter for state and fault estimation of nonlinear system with unknown input
• Variational Bayesian adaptation of process noise covariance matrix in Kalman filtering
• Outlier-robust Kalman filters with mixture correntropy
• Performance Analysis of the Kalman Filter With Mismatched Noise Covariances
• Cubature Kalman Filters
• An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers
• A Novel Robust Gaussian–Student's t Mixture Distribution Based Kalman Filter
• Variational Bayesian Learning Theory

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