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Deterministic Gaussian Filtering based on Herding

In nonlinear recursive Bayesian state estimation, it is in general intractable to maintain the exact posterior density for the state. In order to obtain an approximate solution, it is common to (re-)approximate the posterior density with a Gaussian density. In this paper, we investigate the use of deterministic samples generated by herding in Gaussian filters. Herding is a (relatively) computationally cheap procedure to sequentially generate deterministic samples from probability density functions. We demonstrate the practicability of herding in sample-based nonlinear Kalman filters and Gaussian particle filters in two challenging tracking scenarios.