Name
Title | ||
|---|---|---|
Neighborhood-based regularization of proposal distribution for improving resampling quality in particle filters |
Abstract | ||
|---|---|---|
Particle Filter is a sequential Montecarlo algorithm extensively used for solving estimation problems with non-linear and non-Gaussian features. In spite of its relative simplicity, it is known to suffer some undesired effects that can spoil its performance. Among these problems we can account the one known as sample depletion. This paper reviews the different causes of sample depletion and the many solutions proposed in the existing literature. It also introduces a new strategy for particle resampling which relies in a local linearization of the proposal distribution. The particles drawn using the proposed method are not affected by sample impoverishment and can indirectly lead to better results thanks to a reduction in the plant noise employed, as well to increased performance because of requiring a lower number of particles to achieve same results. |
Year | Venue | Keywords |
|---|---|---|
2011 | Information Fusion | Monte Carlo methods,particle filtering (numerical methods),sampling methods,local linearization,neighborhood-based regularization,nonGaussian feature,nonlinear feature,particle filter,particle resampling,proposal distribution,resampling quality,sample depletion,sequential Monte Carlo algorithm,Particle Filter,estimation,local linearization,regularized,resampling,sample depletion |
Field | DocType | ISBN |
Kernel (linear algebra),Monte Carlo method,Mathematical optimization,Computer science,Particle filter,Regularization (mathematics),Probability distribution,Sampling (statistics),Resampling,Linearization | Conference | 978-1-4577-0267-9 |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Martí | 1 | 9 | 3.45 |
| Jesús García | 2 | 238 | 30.37 |
| José M. Molina | 3 | 604 | 67.82 |