Deterministic and stochastic error modeling of inertial sensors and magnetometers

Simple item page
buir.advisorBarshan, Billur
dc.contributor.authorSeçer, Görkem
dc.date.accessioned2016-01-08T18:24:57Z
dc.date.available2016-01-08T18:24:57Z
dc.date.issued2012
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical refences.en_US
dc.description.abstractThis thesis focuses on the deterministic and stochastic modeling and model parameter estimation of two commonly employed inertial measurement units. Each unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. In the first part of the thesis, deterministic modeling and calibration of the units are performed, based on real test data acquired from a flight motion simulator. The deterministic modeling and identification of accelerometers is performed based on a traditional model. A novel technique is proposed for the deterministic modeling of the gyroscopes, relaxing the test bed requirement and enabling their in-use calibration. This is followed by the presentation of a new sensor measurement model for magnetometers that improves the calibration error by modeling the orientation-dependent magnetic disturbances in a gimbaled angular position control machine. Model-based Levenberg-Marquardt and modelfree evolutionary optimization algorithms are adopted to estimate the calibration parameters of sensors. In the second part of the thesis, stochastic error modeling of the two inertial sensor units is addressed. Maximum likelihood estimation is employed for estimating the parameters of the different noise components of the sensors, after the dominant noise components are identified. Evolutionary and gradient-based optimization algorithms are implemented to maximize the likelihood function, namely particle swarm optimization and gradient-ascent optimization. The performance of the proposed algorithm is verified through experiments and the results are compared to the classical Allan variance technique. The results obtained with the proposed approach have higher accuracy and require a smaller sample data size, resulting in calibration experiments of shorter duration. Finally, the two sensor units are compared in terms of repeatability, present measurement noise, and unaided navigation performance.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:24:57Z (GMT). No. of bitstreams: 1 0006515.pdf: 11817707 bytes, checksum: 5f1a853e4aa8e0fcd7dcd245b6c1e90c (MD5)en
dc.description.statementofresponsibilitySeçer, Görkemen_US
dc.format.extentxiv, 103 leaves, illustrationsen_US
dc.identifier.urihttp://hdl.handle.net/11693/15811
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInertial sensorsen_US
dc.subjectdeterministic error modelingen_US
dc.subjectstochastic error modelingen_US
dc.subjectin-field calibrationen_US
dc.subjectLevenberg-Marquardt algorithmen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectgradient-ascent optimizationen_US
dc.subjectAllan varianceen_US
dc.subjectmaximum likelihood estimationen_US
dc.subject.lccTA165 .S43 2012en_US
dc.subject.lcshDetectors.en_US
dc.subject.lcshMagnetometers.en_US
dc.subject.lcshBiosensors.en_US
dc.subject.lcshMeasurement.en_US
dc.subject.lcshStochastic analysis.en_US
dc.subject.lcshCalibration.en_US
dc.titleDeterministic and stochastic error modeling of inertial sensors and magnetometersen_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0006515.pdf
Size:
11.27 MB
Format:
Adobe Portable Document Format
Download

Collections

Graduate School of Engineering and Science