On Computational Complexity Reduction Methods for Kalman Filter Extensions

Research output: Contribution to journalReview Article


Research units

  • Tampere University


The Kalman filter and its extensions are used in a vast number of aerospace and navigation applications for nonlinear state estimation of time series. In the literature, different approaches have been proposed to exploit the structure of the state and measurement models to reduce the computational demand of the algorithms. In this tutorial, we survey existing code optimization methods and present them using unified notation that allows them to be used with various Kalman filter extensions. We develop the optimization methods to cover a wider range of models, show how different structural optimizations can be combined, and present new applications for the existing optimizations. Furthermore, we present an example that shows that the exploitation of the structure of the problem can lead to improved estimation accuracy while reducing the computational load. This tutorial is intended for persons who are familiar with Kalman filtering and want to get insights for reducing the computational demand of different Kalman filter extensions.


Original languageEnglish
Article number8861457
Pages (from-to)2-19
Number of pages18
JournalIEEE Aerospace and Electronic Systems Magazine
Issue number10
Publication statusPublished - 1 Oct 2019
MoE publication typeA2 Review article in a scientific journal

    Research areas

  • Computational complexity, Kalman filters, Optimisation, State estimation, Time series

ID: 38542701