A uniformly convergent adaptive particle filter

A Uniformly Convergent Adaptive Particle Filter

Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the c...

Self Adaptive Particle Filter

The particle filter has emerged as a useful tool for problems requiring dynamic state estimation. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate these particles at each iteration. Both features are specified beforehand and are kept fixed in the regular implementation of the filter. In ...

Robust Adaptive Unscented Particle Filter

An adaptive color-based particle filter

Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Color distributions are applied, as they are robust to partial occl...

Particle filter with adaptive sample size

The paper deals with the particle filter in state estimation of a discrete-time nonlinear non-Gaussian system. The goal of the paper is to design a sample size adaptation technique to guarantee a quality of a filtering estimate produced by the particle filter which is an approximation of the true filtering estimate. The quality is given by a difference between the approximate filtering estimate...