Observatorio de I+D+i UPM

Descripción
This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes? rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary.
Internacional	Si
Nombre congreso	15th International Conference on Information Fusion, FUSION 2012
Tipo de participación	960
Lugar del congreso	Singapore
Revisores	Si
ISBN o ISSN	978-1-4673-0417-7
DOI
Fecha inicio congreso	07/09/2012
Fecha fin congreso	12/09/2012
Desde la página	479
Hasta la página	486
Título de las actas	Proceedings of 15th International Conference on Information Fusion 2012

Esta actividad pertenece a memorias de investigación

• Autor: Ángel Froilán García Fernández (UPM)
• Autor: M.R. Morelande (University of Melbourne, Australia)
• Autor: Jesus Grajal De la Fuente (UPM)

• Creador: Grupo de Investigación: Microondas y Radar
• Departamento: Señales, Sistemas y Radiocomunicaciones