Adaptive unscented Gaussian likelihood approximation filter

This paper focuses on the update step of Bayesian nonlinear ltering. We rst derive the unscented Gaussian likelihood approximation lter (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 lter (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.