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Nonlinear Multidimensional Bayesian Estimation with Fourier Densities
2006
Proceedings of the 45th IEEE Conference on Decision and Control
Efficiently implementing nonlinear Bayesian estimators is still an unsolved problem, especially for the multidimensional case. A trade-off between estimation quality and demand on computational resources has to be found. Using multidimensional Fourier series as representation for probability density functions, so called Fourier densities, is proposed. To ensure non-negativity, the approximation is performed indirectly via Ψ-densities, of which the absolute square represent the Fourier density.
doi:10.1109/cdc.2006.377378
dblp:conf/cdc/BrunnSH06
fatcat:puod55qva5fzbadd5ui4zqcbw4