Observatorio de I+D+i UPM

Descripción
We devise a filtering algorithm to approximate the first two moments of the posterior probability density function (PDF). The novelties of the algorithm are in the update step. If the likelihood has a bounded support, we can use a modified prior distribution that meets Bayes? rule exactly. Applying a Kalman filter (KF) to the modified prior distribution, referred to as truncated Kalman filter (TKF), can vastly improve the performance of the conventional Kalman filter, particularly when the measurements are informative relative to the prior. The application of the TKF to practical problems in which the measurement noise PDF has unbounded support is achieved by imposing several approximating assumptions which are valid only when the measurements are informative. This implies that we adaptively choose between an approximation to the KF or the TKF according to the information provided by the measurement. The resulting algorithm based on the unscented transformation is referred to as truncated unscented KF.
Internacional	Si
JCR del ISI	Si
Título de la revista	Ieee Transactions on Signal Processing
ISSN	1053-587X
Factor de impacto JCR	2,628
Información de impacto
Volumen	60
DOI
Número de revista	7
Desde la página	3372
Hasta la página	3386
Mes	JULIO
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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