Add to ORKGThis paper presents the development of a new method for parameter estimation in linear state space model. The pro-posed method is based on a Rao-Blackwellised particle filter. The simulation results with a railway vehicle dynamic model are provided which demonstrate the effectiveness of the pro-posed method in comparison with the conventional EKF-based method.
International audienceThe state-space modeling of partially observed dynamical systems generally req...
The PhD thesis deals with the general model based estimation problem, which is solved here using par...
In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE)...
Individual parameters of vehicle dynamic systems were traditionally derived from expensive component...
This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic sys...
International audienceA new approach to parameter estimation of dynamical models is proposed. Its ob...
The state-space modeling of partially observed dynamical systems generally requires estimates of unk...
AbstractIn this paper, the marginal Rao-Blackwellized particle filter (MRBPF), which fuses the Rao-B...
The recently developed particle filter offers a general numerical tool to approximate the state a po...
In this paper, a proposal for the treatment of driving dynamic datasets of a railway vehicle is outl...
Improved calibration of simulation models in railway dynamics: application of a parameter identifica...
Mathematical models simulating the handling behaviour of passenger cars are extensively used at a de...
The problem of combined state and parameter estimation in nonlinear state space models, based on Bay...
For performance gain and efficiency it is important to utilize model structure in particle filtering...
International audienceThe state-space modeling of partially observed dynamical systems generally req...
The PhD thesis deals with the general model based estimation problem, which is solved here using par...
In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE)...
Individual parameters of vehicle dynamic systems were traditionally derived from expensive component...
This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic sys...
International audienceA new approach to parameter estimation of dynamical models is proposed. Its ob...
The state-space modeling of partially observed dynamical systems generally requires estimates of unk...
AbstractIn this paper, the marginal Rao-Blackwellized particle filter (MRBPF), which fuses the Rao-B...
The recently developed particle filter offers a general numerical tool to approximate the state a po...
In this paper, a proposal for the treatment of driving dynamic datasets of a railway vehicle is outl...
Improved calibration of simulation models in railway dynamics: application of a parameter identifica...
Mathematical models simulating the handling behaviour of passenger cars are extensively used at a de...
The problem of combined state and parameter estimation in nonlinear state space models, based on Bay...
For performance gain and efficiency it is important to utilize model structure in particle filtering...
International audienceThe state-space modeling of partially observed dynamical systems generally req...
The PhD thesis deals with the general model based estimation problem, which is solved here using par...
In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE)...