Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models
Year: 2004
Type: article
Abstract: 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 solutions, the extended Kalman filter (EKF) has become one of the most widely used algorithms with applications in state, parameter and dual estimation. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can
DOI: 10.6083/m4z60kz5
Author: Van Der Merwe, Rudolph
Institution: Oregon Health & Science University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1520710862