On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Brief paper: Bayesian estimation via sequential Monte Carlo sampling-Constrained dynamic systems
Automatica (Journal of IFAC)
An improvement on resampling algorithm of particle filters
IEEE Transactions on Signal Processing
Truncation nonlinear filters for state estimation with nonlinear inequality constraints
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A particle algorithm for sequential Bayesian parameter estimationand model selection
IEEE Transactions on Signal Processing
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
Recursive bayesian estimation using gaussian sums
Automatica (Journal of IFAC)
Hi-index | 22.14 |
Nonlinear stochastic dynamical systems are widely used to model physical processes. In many practical applications, the state variables are defined on a compact set of the state space, i.e., they are bounded or saturated. To estimate the states of systems with saturated variables, the Saturated Particle Filter (SPF) has recently been developed. This filter exploits the structure of the saturated system using a specific importance sampling distribution. In this paper we investigate the asymptotic properties of the filter, in particular its almost sure convergence to the true posterior PDF. Furthermore, an improved SPF is developed that uses a novel resampling procedure to overcome the practical shortcomings of the original SPF. We prove that this new filter also converges almost surely to the true posterior PDF. Both versions of the SPF are presented in easy to implement algorithmic forms.