Extended, Unscented Kalman, and Sigma Point Multiple Distribution Estimation Filters for Nonlinear Discrete State-Space Models

Masaya Murata*, Isao Kawano, Koichi Inoue

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The extended and unscented Kalman multiple distribution estimation filters (EKMDEF/UKMDEF) were recently proposed for nonlinear continuous-discrete state-space models and their superior filtering accuracy was shown in the simulation of satellite reentry. The EKMDEF/UKMDEF is based on the multiple distribution estimation (MDE) for the filtered state probability density function (PDF) and this letter provides its alternate derivation with a comparison of the Gaussian sum filter (GSF). This result sheds light on the relationship between the EKMDEF/UKMDEF and the GSF and a new filter that is more stable than the EKMDEF/UKMDEF can be designed. The performance of the proposed filter is examined for nonlinear discrete state-space models using benchmark simulation problems and is compared with those of the representative filters including particle filters (PFs).
Original languageEnglish
Pages (from-to)982 - 987
Number of pages6
JournalIEEE Control Systems Letters
Volume4
Issue number4
DOIs
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed

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