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Memorias de investigación
Ponencias en congresos:
On the Bias of the SIR Filter in Parameter Estimation of the Dynamics Process of State Space Models
Año:2015
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
As a popular nonlinear estimation tool, the sampling importance resampling (SIR) filter has been applied with the expectation?maximization (EM) principle, including the typical maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation, for estimating the parameters of the state space model (SSM). This paper concentrates on an inevitable bias existing in the EM-SIR filter for estimating the dynamics process of the SSM. It is analyzed that the root reason for the bias is the sample impoverishment caused by the resampling procedure employed in the filter. A process noise simulated for the particle propagation that is larger than the real noise involved with the true state will be helpful to counteract sample impoverishment, thereby providing better filtering result. Correspondingly, the EM-SIR filter tends to yield a biased (larger-than-the-truth) estimate of the process noise if it is unknown and needs to be estimated. The bias is elaborated via a straightforward roughening approach by means of both qualitative logical deduction and quantitative numerical simulation. However, it seems hard to fully remove this bias in practice
Internacional
Si
Nombre congreso
Distributed Computing and Artificial Intelligence, 12th International Conference
Tipo de participación
960
Lugar del congreso
Salamanca
Revisores
Si
ISBN o ISSN
978-3-319-19637-4
DOI
Fecha inicio congreso
03/06/2015
Fecha fin congreso
05/06/2015
Desde la página
87
Hasta la página
95
Título de las actas
Distributed Computing and Artificial Intelligence, 12th International Conference
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Javier Bajo Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group
  • Departamento: Inteligencia Artificial
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