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  • The present paper is concerned with the development of an algorithm for the processing of data from gyroscopes and accelerometers such that they together form an attitude sensor. The MATLAB programming environment (only MATLAB in the following) has been chosen for the development of this algorithm. The reason has been the ease with which codes can be created and debugged. With the aid of this programming environment it is also possible to use the not very well known but still simple method of visualizing in real time individual signals from primary sensors as well as the algorithm output, i.e. the sensor attitude in space. One chapter of the paper is therefore devoted to the method of real-time visualization in MATLAB. Signals from the three-axis gyroscope are taken as the primary source of information. A disadvantage of gyroscopes is the incessant small unpredictable change of zero position, which is responsible for the error in attitude determination increasing with time. Using signals from the thre
  • The present paper is concerned with the development of an algorithm for the processing of data from gyroscopes and accelerometers such that they together form an attitude sensor. The MATLAB programming environment (only MATLAB in the following) has been chosen for the development of this algorithm. The reason has been the ease with which codes can be created and debugged. With the aid of this programming environment it is also possible to use the not very well known but still simple method of visualizing in real time individual signals from primary sensors as well as the algorithm output, i.e. the sensor attitude in space. One chapter of the paper is therefore devoted to the method of real-time visualization in MATLAB. Signals from the three-axis gyroscope are taken as the primary source of information. A disadvantage of gyroscopes is the incessant small unpredictable change of zero position, which is responsible for the error in attitude determination increasing with time. Using signals from the thre (en)
Title
  • Inertial Measurement Unit – Data Fusion and Visualization using MATLAB
  • Inertial Measurement Unit – Data Fusion and Visualization using MATLAB (en)
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  • Inertial Measurement Unit – Data Fusion and Visualization using MATLAB
  • Inertial Measurement Unit – Data Fusion and Visualization using MATLAB (en)
skos:notation
  • RIV/00216305:26220/12:PU99988!RIV13-GA0-26220___
http://linked.open...avai/riv/aktivita
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  • P(GD102/09/H081)
http://linked.open...vai/riv/dodaniDat
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  • 141274
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  • RIV/00216305:26220/12:PU99988
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  • Inertial measurement unit, MATLAB, Data fusion, MEMS, Gyroscope, Error correction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [51B357F80E53]
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  • Brno
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  • IFAC-PapersOnLine 10344 Virginia Lee Dr. Centerv
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the IFAC/IEEE PDES 2012 International Conference
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http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Baránek, Radek
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • IFAC-PapersOnLine / Elsevier
https://schema.org/isbn
  • 978-3-902823-21-2
http://localhost/t...ganizacniJednotka
  • 26220
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