Uhlmann, Jeffrey;
Julier, Simon J;
(2022)
Gaussianity and the Kalman Filter: A Simple Yet Complicated Relationship.
Journal de Ciencia e Ingeniería
, 14
(1)
pp. 21-26.
10.46571/jci.2022.1.2.
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Abstract
One of the most common misconceptions made about the Kalman filter when applied to linear systems is that it requires an assumption that all error and noise processes are Gaussian. This misconception has frequently led to the Kalman filter being dismissed in favor of complicated and/or purely heuristic approaches that are supposedly ``more general'' in that they can be applied to problems involving non-Gaussian noise. The fact is that the Kalman filter provides rigorous and optimal performance guarantees that do not rely on any distribution assumptions beyond mean and error covariance information. These guarantees even apply to use of the Kalman update formula when applied with nonlinear models, as long as its other required assumptions are satisfied. Here we discuss misconceptions about its generality that are often found and reinforced in the literature, especially outside the traditional fields of estimation and control.
Type: | Article |
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Title: | Gaussianity and the Kalman Filter: A Simple Yet Complicated Relationship |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.46571/jci.2022.1.2 |
Publisher version: | https://doi.org/10.46571/jci.2022.1.2 |
Language: | English |
Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Educational engineering; engineering history |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10153173 |
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