The variational Bayes method is applied to the state-space estimation problem with maneuvers or changes in the covariance of the observation noise. The resulting algorithm is an off-line batch method that can be used to provide a baseline performance estimation results for the recursive methods. In addition to batch methods we introduce a heuristic approach to make the algorithm on-line. Through simulations we show how the introduced method achieves the best accuracy out of all compared approximative estimation methods
The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system d...
Multiple model filtering has been widely used to handle uncertainties in system dynamics and noise c...
In recent work we have developed a novel variational inference method for partially observed systems...
The variational Bayes method is applied to the state-space estimation problem with maneuvers or chan...
We present an adaptive smoother for linear state-space models with unknown process and measurement n...
This paper is considered with joint estimation of state and time-varying noise covariance matrices i...
The uncertainty of noise statistics in dynamic systems is one of the most important issues in engine...
In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gau...
In this technical report, some derivations for the filter and smoother proposed in [1] are presented...
International audienceUsing Kalman techniques, it is possible to perform optimal estimation in linea...
This paper proposes an event-triggered variational Bayesian filter for remote state estimation with ...
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy...
In recent work we have developed a novel variational inference method for partially observed systems...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
In this paper, we investigate the state estimation of systems with unknown covariance non-Gaussian m...
The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system d...
Multiple model filtering has been widely used to handle uncertainties in system dynamics and noise c...
In recent work we have developed a novel variational inference method for partially observed systems...
The variational Bayes method is applied to the state-space estimation problem with maneuvers or chan...
We present an adaptive smoother for linear state-space models with unknown process and measurement n...
This paper is considered with joint estimation of state and time-varying noise covariance matrices i...
The uncertainty of noise statistics in dynamic systems is one of the most important issues in engine...
In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gau...
In this technical report, some derivations for the filter and smoother proposed in [1] are presented...
International audienceUsing Kalman techniques, it is possible to perform optimal estimation in linea...
This paper proposes an event-triggered variational Bayesian filter for remote state estimation with ...
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy...
In recent work we have developed a novel variational inference method for partially observed systems...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
In this paper, we investigate the state estimation of systems with unknown covariance non-Gaussian m...
The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system d...
Multiple model filtering has been widely used to handle uncertainties in system dynamics and noise c...
In recent work we have developed a novel variational inference method for partially observed systems...