Name
Abstract | ||
|---|---|---|
Particle filtering (PF) is a powerful method to estimate the posterior distribution in nonlinear/ non Gaussian state space models. To overcome the curse of dimensionality of PF, the block PF (BPF) partitions the state space and runs correction and resampling steps separately on each subspace. Using a blocking step can significantly reduce the variance of the filtering distribution estimate, but it breaks correlation across subspaces.In this paper, we introduce a parallelisation scheme in the block PF. The scheme consists in dispatching the set of particles into M parallel BPFs. We show that the usual benefit of parallelisation in terms of bias-variance trade-off remains valid and, most importantly, that assigning different partitions to the parallel filters leads to far better performance than naive parallelisation using only one partition. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SSP49050.2021.9513788 | 2021 IEEE Statistical Signal Processing Workshop (SSP) |
Keywords | DocType | ISSN |
Bayesian inference,particle filter,high dimension,blocking,parallelisation | Conference | 2373-0803 |
ISBN | Citations | PageRank |
978-1-7281-5768-9 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Min | 1 | 0 | 0.34 |
| Christelle Garnier | 2 | 0 | 0.34 |
| François Septier | 3 | 0 | 1.01 |
| John Klein | 4 | 0 | 1.01 |