Adaptive unscented Gaussian likelihood approximation filter

• Ángel F. García-Fernández ^[1] ; Mark R. Morelande ^[2] ; Jesús Grajal ^[1] ; Lennart Svensson ^[3]
  1. [1] Universidad Politécnica de Madrid

     Universidad Politécnica de Madrid

     Madrid, España

     
  2. [2] Royal Melbourne Institute of Technology University

     Royal Melbourne Institute of Technology University

     Australia

     
  3. [3] Chalmers University of Technology

     Chalmers University of Technology

     Suecia

     

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• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 54, 2015, págs. 166-175
• Idioma: inglés
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• Resumen

  • 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.