Work

An Improved Cubature Kalman Filter for GNSS-Denied and System-Noise-Varying INS/GNSS Navigation

Year: 2025

Type: article

Abstract: The degradation of nonlinear filtering in INS/GNSS integrated navigation due to missing GNSS observations and system noise uncertainty is addressed in this paper. An improved cubature Kalman filter (ICKF) is proposed, leveraging a modified cubature point update framework (MUF) and the maximum likelihood (ML) principle. In the ICKF, the ML principle is employed to estimate the process noise covariance, which is then integrated into the MUF to construct the posterior cubature points directly, bypassing the need for resampling. As the process noise covariance is updated in real time, and the prediction cubature points' error is directly transferred to the posterior cubature points, the proposed algorithm demonstrates reduced sensitivity to missing observations and system noise uncertainty. The effectiveness of the proposed algorithm has been validated through both simulation and practical experiments.

Source: Micromachines

DOI: 10.3390/mi16101116

Authors: Di Liu, Xiyuan Chen, Bingbo Cui

Institutions: Qilu University of Technology, Shandong Academy of Sciences, Southeast University, Jiangsu University

Language: English

Topic: Inertial Sensor and Navigation

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,534

Funder: Natural Science Foundation of Shandong Province