Application of fractional sensor fusion algorithms for inertial mems sensing
The work presents an extension of the conventional Kalman filtering concept for systems of fractional order (FOS). Modifications are introduced using the Grünwald‐Letnikov (GL) definition of the fractional derivative (FD) and corresponding truncation of the history length. Two versions of the fracti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2009-06-01
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| Series: | Mathematical Modelling and Analysis |
| Subjects: | |
| Online Access: | https://journals.vgtu.lt/index.php/MMA/article/view/6539 |
| _version_ | 1818787816988999680 |
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| author | Michailas Romanovas Lasse Klingbeil Martin Traechtler Yiannos Manoli |
| author_facet | Michailas Romanovas Lasse Klingbeil Martin Traechtler Yiannos Manoli |
| author_sort | Michailas Romanovas |
| collection | DOAJ |
| description | The work presents an extension of the conventional Kalman filtering concept for systems of fractional order (FOS). Modifications are introduced using the Grünwald‐Letnikov (GL) definition of the fractional derivative (FD) and corresponding truncation of the history length. Two versions of the fractional Kalman filter (FKF) are shown, where the FD is calculated directly or by augmenting the state vector with the estimate of the FD. The filters are compared to conventional integer order (IO) Position (P‐KF) and Position‐Velocity (PV‐KF) Kalman filters as well as to an adaptive Interacting Multiple‐Model Kalman Filter (IMM‐KF). The performance of the filters is assessed based on a hand and a head motion data set. The feasibility of the given approach is shown.
First published online: 14 Oct 2010 |
| first_indexed | 2024-12-18T14:13:48Z |
| format | Article |
| id | doaj.art-1a1b464970a04a0abcb4655a4e56153e |
| institution | Directory Open Access Journal |
| issn | 1392-6292 1648-3510 |
| language | English |
| last_indexed | 2024-12-18T14:13:48Z |
| publishDate | 2009-06-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Mathematical Modelling and Analysis |
| spelling | doaj.art-1a1b464970a04a0abcb4655a4e56153e2022-12-21T21:05:03ZengVilnius Gediminas Technical UniversityMathematical Modelling and Analysis1392-62921648-35102009-06-0114210.3846/1392-6292.2009.14.199-209Application of fractional sensor fusion algorithms for inertial mems sensingMichailas Romanovas0Lasse Klingbeil1Martin Traechtler2Yiannos Manoli3HSG-IMIT Institute of Micromachining and Information Technology Wilhelm-Schickard-Straße 10, D78052, Villingen-Schwenningen, Germany; Chair of Microelectronics, Department of Microsystems Engineering IMTEK), University of FreiburgHSG-IMIT Institute of Micromachining and Information Technology Wilhelm-Schickard-Straße 10, D78052, Villingen-Schwenningen, GermanyHSG-IMIT Institute of Micromachining and Information Technology Wilhelm-Schickard-Straße 10, D78052, Villingen-Schwenningen, GermanyHSG-IMIT Institute of Micromachining and Information Technology Wilhelm-Schickard-Straße 10, D78052, Villingen-Schwenningen, Germany; Chair of Microelectronics, Department of Microsystems Engineering IMTEK), University of Freiburg Georges-Köhler-Allee 101, D79110, Freiburg, GermanyThe work presents an extension of the conventional Kalman filtering concept for systems of fractional order (FOS). Modifications are introduced using the Grünwald‐Letnikov (GL) definition of the fractional derivative (FD) and corresponding truncation of the history length. Two versions of the fractional Kalman filter (FKF) are shown, where the FD is calculated directly or by augmenting the state vector with the estimate of the FD. The filters are compared to conventional integer order (IO) Position (P‐KF) and Position‐Velocity (PV‐KF) Kalman filters as well as to an adaptive Interacting Multiple‐Model Kalman Filter (IMM‐KF). The performance of the filters is assessed based on a hand and a head motion data set. The feasibility of the given approach is shown. First published online: 14 Oct 2010https://journals.vgtu.lt/index.php/MMA/article/view/6539Kalman filterfractional‐order systemfractional filteringsensor fusionGrünwald‐Letnikov derivative |
| spellingShingle | Michailas Romanovas Lasse Klingbeil Martin Traechtler Yiannos Manoli Application of fractional sensor fusion algorithms for inertial mems sensing Mathematical Modelling and Analysis Kalman filter fractional‐order system fractional filtering sensor fusion Grünwald‐Letnikov derivative |
| title | Application of fractional sensor fusion algorithms for inertial mems sensing |
| title_full | Application of fractional sensor fusion algorithms for inertial mems sensing |
| title_fullStr | Application of fractional sensor fusion algorithms for inertial mems sensing |
| title_full_unstemmed | Application of fractional sensor fusion algorithms for inertial mems sensing |
| title_short | Application of fractional sensor fusion algorithms for inertial mems sensing |
| title_sort | application of fractional sensor fusion algorithms for inertial mems sensing |
| topic | Kalman filter fractional‐order system fractional filtering sensor fusion Grünwald‐Letnikov derivative |
| url | https://journals.vgtu.lt/index.php/MMA/article/view/6539 |
| work_keys_str_mv | AT michailasromanovas applicationoffractionalsensorfusionalgorithmsforinertialmemssensing AT lasseklingbeil applicationoffractionalsensorfusionalgorithmsforinertialmemssensing AT martintraechtler applicationoffractionalsensorfusionalgorithmsforinertialmemssensing AT yiannosmanoli applicationoffractionalsensorfusionalgorithmsforinertialmemssensing |