Observatorio de I+D+i UPM

Descripción
This paper focuses on the update step of Bayesian nonlinear filtering. We first derive the unscented Gaussian likelihood approximation filter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UGLAF approximation is accurate in the cases where the unscented Kalman filter (UKF) is not and the other way round. As a result, we propose the adaptive UGLAF (AUGLAF), which selects the best approximation to the posterior (UKF or UGLAF) based on the Kullback?Leibler divergence. This enables AUGLAF to outperform both the UKF and UGLAF.
Internacional	Si
JCR del ISI	Si
Título de la revista	Automatica
ISSN	0005-1098
Factor de impacto JCR	3,02
Información de impacto
Volumen	54
DOI	10.1016/j.automatica.2015.02.005
Número de revista
Desde la página	166
Hasta la página	175
Mes	SIN MES
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• Autor: Ángel F. García Fernández (Curtin University, Perth, Australia)
• Autor: Mark R. Morelande (RMIT University, Australia)
• Autor: Jesus Grajal De la Fuente (UPM)
• Autor: Lennart Svensson (Chalmers University of Technology, Gothenburg, Sweden)

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