Adaptive unscented Gaussian likelihood approximation filter
Artikel i vetenskaplig tidskrift, 2015

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.

Bayes' rule

Gaussian approximation

Kalman filter

Nonlinear filtering


Angel Garcia

Universidad Politecnica de Madrid

M. R. Morelande

RMIT University

J. Grajal

Universidad Politecnica de Madrid

Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling


0005-1098 (ISSN)

Vol. 54 166-175





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