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

## TL;DR

This paper introduces an improved cubature Kalman filter to enhance navigation accuracy when GPS signals are lost or system noise varies.

## Contribution

The novelty lies in combining a modified cubature point update framework with maximum likelihood estimation for real-time noise adaptation.

## Key findings

- The proposed ICKF reduces sensitivity to missing GNSS observations and system noise uncertainty.
- Simulation and practical experiments confirm the improved algorithm's effectiveness in maintaining navigation accuracy.
- Real-time process noise covariance estimation enhances robustness in challenging environments.

## 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.