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On the performance of parallelisation schemes for particle filtering

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.contributor.authorMíguez Arenas, Joaquín
dc.contributor.authorRíos Muñoz, Gonzalo Ricardo
dc.contributor.authorCrisan, Dan
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-11-13T16:57:40Z
dc.date.available2023-11-13T16:57:40Z
dc.date.issued2018-05-25
dc.description.abstractConsiderable effort has been recently devoted to the design of schemes for the parallel implementation of sequential Monte Carlo (SMC) methods for dynamical systems, also widely known as particle filters (PFs). In this paper, we present a brief survey of recent techniques, with an emphasis on the availability of analytical results regarding their performance. Most parallelisation methods can be interpreted as running an ensemble of lower-cost PFs, and the differences between schemes depend on the degree of interaction among the members of the ensemble. We also provide some insights on the use of the simplest scheme for the parallelisation of SMC methods, which consists in splitting the computational budget into M non-interacting PFs with N particles each and then obtaining the desired estimators by averaging over the M independent outcomes of the filters. This approach minimises the parallelisation overhead yet still displays desirable theoretical properties. We analyse the mean square error (MSE) of estimators of moments of the optimal filtering distribution and show the effect of the parallelisation scheme on the approximation error rates. Following these results, we propose a time-error index to compare schemes with different degrees of parallelisation. Finally, we provide two numerical examples involving stochastic versions of the Lorenz 63 and Lorenz 96 systems. In both cases, we show that the ensemble of non-interacting PFs can attain the approximation accuracy of a centralised PF (with the same total number of particles) in just a fraction of its running time using a standard multicore computer.en
dc.description.sponsorshipThis work was partially supported by Ministerio de Economía y Competitividad of Spain (TEC2012-38883-C02-01 COMPREHENSION and TEC2015-69868-C2-1-R ADVENTURE) and the Office of Naval Research Global (N62909- 15-1-2011). D. C. and J. M. acknowledge the support of the Isaac Newton Institute through the program Monte Carlo Inference for High-Dimensional Statistical Models.en
dc.format.extent18es
dc.format.mimetypeapplication/pdfen
dc.identifier.bibliographicCitationCrisan, D., Míguez, J., & Ríos-Muñoz, G. (2018). On the performance of parallelisation schemes for particle filtering. EURASIP Journal on Advances in Signal Processing 2018(1).en
dc.identifier.doi10.1186/s13634-018-0552-x
dc.identifier.issn1687-6172
dc.identifier.publicationfirstpage1es
dc.identifier.publicationlastpage18es
dc.identifier.publicationtitleEURASIP Journal on Advances in Signal Processingen
dc.identifier.publicationvolume2018es
dc.identifier.urihttps://hdl.handle.net/10016/38841
dc.identifier.uxxiAR/0000021559
dc.language.isoengen
dc.publisherSpringeres
dc.relation.projectIDGobierno de España. TEC2012-38883-C02-01es
dc.relation.projectIDInternacional. N62909-15-1-2011es
dc.relation.projectIDGobierno de España. TEC2015-69868-C2-1-Res
dc.rights© 2018, The Author(s)en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.otherParticle filteringen
dc.subject.otherParallelisationen
dc.subject.otherConvergence analysisen
dc.subject.otherParticle islandsen
dc.subject.otherLorenz 63en
dc.subject.otherLorenz 96en
dc.titleOn the performance of parallelisation schemes for particle filteringen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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