Variational Bayesian Approximations Kalman Filter Based on Threshold Judgment

TL;DR

This paper introduces a threshold-based variational Bayesian Kalman filter that efficiently estimates non-Gaussian measurement noise parameters online, improving accuracy and reducing computational complexity.

Contribution

It presents a novel threshold-based approach combined with variational Bayesian estimation for online noise parameter estimation in non-Gaussian models.

Findings

Accurately estimates state and noise parameters in simulations.

Demonstrates effectiveness over traditional methods.

Reduces computational complexity in non-Gaussian noise estimation.

Abstract

The estimation of non-Gaussian measurement noise models is a significant challenge across various fields. In practical applications, it often faces challenges due to the large number of parameters and high computational complexity. This paper proposes a threshold-based Kalman filtering approach for online estimation of noise parameters in non-Gaussian measurement noise models. This method uses a certain amount of sample data to infer the variance threshold of observation parameters and employs variational Bayesian estimation to obtain corresponding noise variance estimates, enabling subsequent iterations of the Kalman filtering algorithm. Finally, we evaluate the performance of this algorithm through simulation experiments, demonstrating its accurate and effective estimation of state and noise parameters.