Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models

Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. 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 online. This posterior density constitutes the complete solution to the probabilistic inference problem, and allows us to calculate any "optimal " estimate of the state. Unfortunately, for most real-world problems, the optimal Bayesian recursion is intractable and approximate solutions must be used. Within the space of approximate…