Reduction of prediction error sensitivity to parameters in Kalman filter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00355728" target="_blank" >RIV/68407700:21230/22:00355728 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21720/22:00355728

Výsledek na webu

<a href="https://doi.org/10.1016/j.jfranklin.2021.12.019" target="_blank" >https://doi.org/10.1016/j.jfranklin.2021.12.019</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jfranklin.2021.12.019" target="_blank" >10.1016/j.jfranklin.2021.12.019</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reduction of prediction error sensitivity to parameters in Kalman filter

Popis výsledku v původním jazyce

The desensitized Kalman filter is a practical and intuitive robust filtering method. However, a thorough analysis of its stability and impact of assumptions is missing. This paper expands the theory of desensitized Kalman filtering by proposing a stochastic approach to reduce estimation error sensitivity to parameters. The novel approach leads to the exact desensitized Kalman filter that does not neglect the gain sensitivity to a parameter. The suboptimal form equivalent to the original desensitized Kalman filter in a special form is proposed. The stability analysis and the definition of stability conditions are possible due to the proposed form that can be interpreted as the Kalman filter with correlated process and measurement noise with time-variant statistics. Furthermore, adaptive normalization of objectives is introduced, which improves the desensitizing performance.

Název v anglickém jazyce

Reduction of prediction error sensitivity to parameters in Kalman filter

Popis výsledku anglicky

The desensitized Kalman filter is a practical and intuitive robust filtering method. However, a thorough analysis of its stability and impact of assumptions is missing. This paper expands the theory of desensitized Kalman filtering by proposing a stochastic approach to reduce estimation error sensitivity to parameters. The novel approach leads to the exact desensitized Kalman filter that does not neglect the gain sensitivity to a parameter. The suboptimal form equivalent to the original desensitized Kalman filter in a special form is proposed. The stability analysis and the definition of stability conditions are possible due to the proposed form that can be interpreted as the Kalman filter with correlated process and measurement noise with time-variant statistics. Furthermore, adaptive normalization of objectives is introduced, which improves the desensitizing performance.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA18-26278S" target="_blank" >GA18-26278S: Zahrnutí apriorní informace při identifikaci nelineárních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

ISSN

0016-0032

e-ISSN

1879-2693

Svazek periodika

359

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

1303-1326

Kód UT WoS článku

000801856900010

EID výsledku v databázi Scopus

2-s2.0-85123007519