Robust Variational Bayesian Filter for Accurate Flight Altitude Estimation with Uncertain Measurement Noise
Caiqin Guo, Kai Shen, Deqing Huang · 2024
Accurate altitude information is essential for achieving safe flight, especially during take-off and landing. The Kalman filter (KF) is an optimal state estimation method that can be utilized for flight altitude estimation. In practical scenario, considering the influence of sensor characteristics and changes in the external environment, uncertain measurement phenomena may arise, which leads to the deviation between the estimated filter state and the true state, leading to filter divergence. In this paper, a robust measurement distribution-based variational Bayesian filtering algorithm is presented as a solution to this issue. The heavy-tailed properties of the measurement noise are modeled using the Student-t distribution, while the variational Bayesian approach is employed to dynamically estimate the statistical characteristics of the measurement noise in real time. Simulation and experiment results demonstrate that the proposed algorithm outperforms both the traditional KF algorithm and the variational Bayesian Adaptive KF (VBAKF) algorithm when dealing with uncertain measurement noise.