  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.
