Name
Abstract | ||
|---|---|---|
Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles can in this way be expressed by a few informative coefficients that can be efficiently communicated between the local processing units. Experiments on a shared-memory multicore processor using up to eight cores show that a linear speedup in the number of used cores is achieved. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ACC.2015.7171939 | ACC |
Field | DocType | ISSN |
Monte Carlo method,Markov chain Monte Carlo,Computer science,Control theory,Parallel computing,Particle filter,Algorithm,Hybrid Monte Carlo,Series expansion,Multi-core processor,Recursion,Speedup | Conference | 0743-1619 |
ISBN | Citations | PageRank |
978-1-4799-8685-9 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olov Rosen | 1 | 23 | 4.10 |
| Alexander Medvedev | 2 | 72 | 22.43 |