A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices
In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for
linear Gaussian state-space models with inaccurate process and measurement noise …
linear Gaussian state-space models with inaccurate process and measurement noise …
[HTML][HTML] Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond
Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …
devoted to time series state space models for a large variety of dynamic estimation …
State estimation methods in navigation: Overview and application
This article deals with state estimation of nonlinear stochastic dynamic systems. The stress is
laid on general introduction of the selected estimation methods, description of their …
laid on general introduction of the selected estimation methods, description of their …
A Novel Robust Student's t-Based Kalman Filter
A novel robust Student's t-based Kalman filter is proposed by using the variational Bayesian
approach, which provides a Gaussian approximation to the posterior distribution. Simulation …
approach, which provides a Gaussian approximation to the posterior distribution. Simulation …
A Novel Robust Gaussian–Student's t Mixture Distribution Based Kalman Filter
Y Huang, Y Zhang, Y Zhao… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In this paper, a novel Gaussian-Student's t mixture (GSTM) distribution is proposed to model
non-stationary heavy-tailed noises. The proposed GSTM distribution can be formulated as a …
non-stationary heavy-tailed noises. The proposed GSTM distribution can be formulated as a …
A novel outlier-robust Kalman filtering framework based on statistical similarity measure
In this article, a statistical similarity measure is introduced to quantify the similarity between
two random vectors. The measure is, then, employed to develop a novel outlier-robust …
two random vectors. The measure is, then, employed to develop a novel outlier-robust …
Bayesian Inference for State-Space Models With Student-t Mixture Distributions
This article proposes a robust Bayesian inference approach for linear state-space models
with nonstationary and heavy-tailed noise for robust state estimation. The predicted …
with nonstationary and heavy-tailed noise for robust state estimation. The predicted …
Robust Kalman filters based on Gaussian scale mixture distributions with application to target tracking
In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian
heavy-tailed and/or skewed state and measurement noises is proposed through modeling …
heavy-tailed and/or skewed state and measurement noises is proposed through modeling …
Robust student'st based nonlinear filter and smoother
Novel Student's t based approaches for formulating a filter and smoother, which utilize heavy
tailed process and measurement noise models, are found through approximations of the …
tailed process and measurement noise models, are found through approximations of the …
A novel Kullback–Leibler divergence minimization-based adaptive student's t-filter
Y Huang, Y Zhang, JA Chambers - IEEE Transactions on signal …, 2019 - ieeexplore.ieee.org
In this paper, in order to improve the Student's t-matching accuracy, a novel Kullback-Leibler
divergence (KLD) minimization-based matching method is firstly proposed by minimizing the …
divergence (KLD) minimization-based matching method is firstly proposed by minimizing the …