Show simple item record

dc.contributor.authorGarcia Fernandez, Angel
dc.contributor.authorMorelande, M.
dc.contributor.authorGrajal, J.
dc.contributor.authorSvensson, L.
dc.identifier.citationGarcia Fernandez, A. and Morelande, M. and Grajal, J. and Svensson, L. 2015. Adaptive unscented Gaussian likelihood approximation filter. Automatica. 54: pp. 166-175.

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.

dc.publisherPergamon Press
dc.titleAdaptive unscented Gaussian likelihood approximation filter
dc.typeJournal Article
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record