Misc,
The Unscented Kalman Filter for Nonlinear Estimation
(2000)
Abstract
The Extended Kalman Filter (EKF) has become a standard
technique used in a number of nonlinear estimation and machine
learning applications. These include estimating the
state of a nonlinear dynamic system, estimating parameters
for nonlinear system identification (e.g., learning the
weights of a neural network), and dual estimation (e.g., the
Expectation Maximization (EM) algorithm) where both states
and parameters are estimated simultaneously.
This paper points out the flaws in using the...
