A Survey of Code Optimization Methods for Kalman Filter Extensions
Matti Raitoharju, Robert Piché · arXiv (Cornell University) · 2015
Kalman filter extensions are commonly used algorithms for nonlinear state estimation in time series. The structure of the state and measurement models in the estimation problem can be exploited to reduce the computational demand of the algorithms. We review algorithms that use different forms of structure and show how they can be combined. We show also that the exploitation of the structure of the problem can lead to improved accuracy of the estimates while reducing the computational load.