Kalman filtering with empirical noise models

Matti Raitoharju, Henri Nurminen, Demet Cilden-Guler, Simo Särkkä

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


Most Kalman filter extensions assume Gaussian noise and when the noise is non-Gaussian, usually other types of filters are used. These filters, such as particle filter variants, are computationally more demanding than Kalman type filters. In this paper, we present an algorithm for building models and using them with a Kalman type filter when there is empirically measured data of the measurement errors. The paper evaluates the proposed algorithm in three examples. The first example uses simulated Student-t distributed measurement errors and the proposed algorithm is compared with algorithms designed specifically for Student-t distribution. Last two examples use real measured errors, one with real data from an Ultra Wideband (UWB) ranging system, and the other using low-Earth orbiting satellite magnetometer measurements. The results show that the proposed algorithm is more accurate than algorithms that use Gaussian assumptions and has similar accuracy to algorithms that are specifically designed for a certain probability distribution.

Original languageEnglish
Title of host publicationProceedings of International Conference on Localization and GNSS, ICL-GNSS 2021
EditorsJari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov
Number of pages7
ISBN (Electronic)978-1-7281-9644-2
Publication statusPublished - Jun 2021
MoE publication typeA4 Conference publication
EventInternational Conference on Localization and GNSS - Tampere, Finland
Duration: 1 Jun 20213 Jun 2021

Publication series

NameInternational Conference on Localization and GNSS
ISSN (Electronic)2325-0771


ConferenceInternational Conference on Localization and GNSS
Abbreviated titleICL-GNSS


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