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Sensor fusion application to railway odometry: "It can't be THAT hard measuring speed..."
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This Master's thesis describes the implementation of an aided InertialNavigation System (INS) using accelerometers, gyroscopes, Global NavigationSatellite Systems, Tachometers and Doppler radars for a railwayvehicle. The system uses a Indirect Error State Information Filter tocorrect the raw integrated measurements from the Inertial MeasurementUnit (IMU) to establish traveled distance, current velocity and currentheading in the Navigation (Earth based) frame.The INS is implemented in Simulink and evaluated using eld datafrom a commercial train running in Switzerland, as well as data fromthe authors of the lter design.The results suggest that all three position dimensions need to bebounded during long runs due to gyro drift, and that calibrating theIMU to transform measurements to the vehicle coordinate system is amajor contributor to the accuracy of the estimates.

Abstract [sv]

Sensor Fusion för Odometri på Järnväg. Detta examensarbete beskriver en implementation av ett Navigationssystem,med accelerometrar, gyroskop, Global Navigation Satellite Systems,Tachometrar samt Doppler radars, for jarnvagsfordon. Systemet anvander ett Indirekt Felbaserat Information Filter for att korrigera integreradematningar fran accelerometer och gyro for att skatta fardad stracka,nuvarande hastighet, samt riktning i ett jordbaserat koordinatsystem. Systemet ar implementerat i Simulink och utvarderat med data fran ett Schweiziskt kommersiellt tag, samt data fran forfattarna till systemarkitekturen. Resultaten antyder att alla tre positionsfrihetsgrader maste varabegransade for att undvika divergens vid langre korningar pa grund avgyrodrift, samt att kalibrering av accelerometer och gyroskop till attvisa matningar linjerade med fordonets axlar ger ett stort bidrag till estimatens nogrannhet.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-153942OAI: diva2:754317
Available from: 2014-11-21 Created: 2014-10-10 Last updated: 2014-11-21Bibliographically approved

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