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Adaptive unscented Gaussian likelihood approximation filter

Angel Garcia-Fernandez ; M. R. Morelande ; J. Grajal ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling)
Automatica (0005-1098). Vol. 54 (2015), p. 166-175.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Bayes' rule, Kalman filter, Gaussian approximation, Nonlinear filtering



Denna post skapades 2015-05-21. Senast ändrad 2015-12-17.
CPL Pubid: 217400

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Signalbehandling

Chalmers infrastruktur