Sigma-point kalman filters for probabilistic inference in dynamic state-space models
@inproceedings{vanderMerwe2004SigmapointKF,
title={Sigma-point kalman filters for probabilistic inference in dynamic state-space models},
author={Rudolph van der Merwe and Eric A. Wan},
year={2004}
}Probabilistic inference is the problem of estimating the hidden variables (states or parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete observations of the system becomes available online. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive. This posterior density constitutes the complete solution to the probabilistic…
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