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master's thesis
Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki
2017. urn:nbn:hr:200:613607
Zubčić, Matej
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Computer Engineering and Automation
Chair of Computer Engineering

Cite this document

Zubčić, M. (2017). Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:613607

Zubčić, Matej. "Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:200:613607

Zubčić, Matej. "Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:200:613607

Zubčić, M. (2017). 'Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 16 May 2021, https://urn.nsk.hr/urn:nbn:hr:200:613607

Zubčić M. Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2017 [cited 2021 May 16] Available at: https://urn.nsk.hr/urn:nbn:hr:200:613607

M. Zubčić, "Određivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:200:613607

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Metadata
TitleOdređivanje položaja objekta u prostoru na temelju sinergije višesenzorskih prostorno vremenskih značajki
Title (english)The positioning of the object in space based on the synergy of multisensor spatio-temporal features
AuthorMatej Zubčić
MentorTomislav Keser (mentor)
Committee memberTomislav Keser (predsjednik povjerenstva)
Committee memberAlfonzo Baumgartner (član povjerenstva)
Committee memberKrešimir Nenadić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Computer Engineering and Automation)
(Chair of Computer Engineering)
Osijek
Defense date and country2017-10-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Process Computing
Universal decimal classification
(UDC)
Abstract
U ovome radu objašnjen je problem određivanja položaja na temelju vektorskih mjerenja senzora. S obzirom na problematiku korištenih senzora pri određivanju položaja, kao estimator položaja izabran je multiplikativni prošireni Kalmanov filter zbog svojih prednosti. Prednosti ovog filtera su računska jednostavnost s obzirom kako su jednadžbe filtera razvijene na temelju kvaternionske parametrizacije položaja. Dodatna prednost ovog filtera je i ta, da filter ne estimira dinamiku stanja tijela, nego samo stanje pogrešaka položaja koje se dodaju na nominalno stanje sustava, a to dodatno ubrzava iteraciju algoritma. Rezultat ovog rada je uspješna implementacija MEKF algoritma u Matlabu, što je i potvrđeno kroz pokuse i mjerenja s obzirom na referentne skupove mjerenja. Rad daje i objašnjenje problema prilikom korištenja izabranog referentnog skupa mjerenja.
Abstract (english)
This thesis explains attitude determination problem based on sensor measurements in vector space. It gives a short overview of deterministic methods used to solve this problem and then progresses with the state space stochastic methods, mainly using the Multiplicative Extended Kalman Filter. The MEKF was chosen due to its advantages, which include, lower computational requirements since state equations are developed using quaternion parametrization of attitude, hence there is no trigonometric operations used in the state transition equation. One additional advantage of this filter is its complementary or indirect nature, i.e. we are not estimating the full-state of the system, just the error-states which brings additional simplifications and robustness when using MEKF for attitude estimation. The result of this work is a successful implementation of the MEKF algorithm in a discrete form, using Matlab, this form is suitable for calculation on digital computers or embedded systems. Implementation is evaluated through comparison of attitude estimates with highly accurate reference measurements. Unfortunately, due to a mistake in the experiments when the reference measurements were gathered, it wasn’t possible to correctly evaluate accuracy of the estimated yaw angle.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:613607
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2018-05-28 10:54:40