Progressive Bayesian estimation with deterministic particles

This paper introduces an enhanced method for progressive Bayesian estimation based on a set of deterministic samples. The information of a given measurement is gradually introduced in order to avoid particle degeneration, which is usually encountered in standard particle filters. The main contribution of this paper is to derive a new method for exploiting smoothness assumptions about the unknown underlying density function of the state.

[1]  U. Hanebeck,et al.  LRKF Revisited: The Smart Sampling Kalman Filter (S2KF) , 2015 .

[2]  Juan M. Corchado,et al.  Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches , 2013, Expert Syst. Appl..

[3]  Uwe D. Hanebeck,et al.  The Smart Sampling Kalman Filter with Symmetric Samples , 2015, ArXiv.

[4]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[5]  Jerome Spanier,et al.  Quasi-Random Methods for Estimating Integrals Using Relatively Small Samples , 1994, SIAM Rev..

[6]  Uwe D. Hanebeck.  Optimal Reduction of Multivariate Dirac Mixture Densities , 2015, Autom..

[7]  Uwe D. Hanebeck,et al.  Dirac mixture approximation of multivariate Gaussian densities , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[8]  N. Oudjane,et al.  Progressive correction for regularized particle filters , 2000, Proceedings of the Third International Conference on Information Fusion.

[9]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[10]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[11]  Uwe D. Hanebeck,et al.  Progressive Bayes: a new framework for nonlinear state estimation , 2003, SPIE Defense + Commercial Sensing.

[12]  Uwe D. Hanebeck,et al.  Progressive Gaussian filtering using explicit likelihoods , 2014, 17th International Conference on Information Fusion (FUSION).

[13]  Uwe D. Hanebeck,et al.  Progressive correction for deterministic Dirac mixture approximations , 2011, 14th International Conference on Information Fusion.