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Calibration of a MEMS inertial measurement unit
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0002-2718-0262
2006 (English)In: Proc. XVII IMEKO World Congress, (Rio de Janeiro, Brazil), Sept.2006, 2006Conference paper, Published paper (Refereed)
Abstract [en]

An approach for calibrating a low-cost IMU isstudied, requiring no mechanical platform for the accelerometercalibration and only a simple rotating table for the gyrocalibration. The proposed calibration methods utilize the factthat ideally the norm of the measured output of the accelerometerand gyro cluster are equal to the magnitude of appliedforce and rotational velocity, respectively. This fact, togetherwith model of the sensors is used to construct a cost function,which is minimized with respect to the unknown model parametersusing Newton’s method. The performance of the calibrationalgorithm is compared with the Cram´er-Rao bound forthe case when a mechanical platform is used to rotate the IMUinto different precisely controlled orientations. Simulation resultsshows that the mean square error of the estimated sensormodel parameters reaches the Cram´er-Rao bound within8 dB, and thus the proposed method may be acceptable for awide range of low-cost applications.

Place, publisher, year, edition, pages
2006.
Keyword [en]
Inertial measurement unit, MEMS sensors, Calibration
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-7727Scopus ID: 2-s2.0-84877782606ISBN: 978-162276646-8 (print)OAI: oai:DiVA.org:kth-7727DiVA: diva2:12838
Conference
XVII IMEKO World Congress, (Rio de Janeiro, Brazil), 17-22 September 2006
Note

QC 20141105

Available from: 2007-11-25 Created: 2007-11-25 Last updated: 2014-11-05Bibliographically approved
In thesis
1. GNSS-aided INS for land vehicle positioning and navigation
Open this publication in new window or tab >>GNSS-aided INS for land vehicle positioning and navigation
2007 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis begins with a survey of current state-of-the art in-car navigation systems. The pros and cons of the four commonly used information sources — GNSS/RF-based positioning, vehicle motion sensors, vehicle models and map information — are described. Common filters to combine the information from the various sources are discussed.

Next, a GNSS-aided inertial navigation platform is presented, into which further sensors such as a camera and wheel-speed encoder can be incorporated. The construction of the hardware platform, together with an extended Kalman filter for a closed-loop integration between the GNSS receiver and the inertial navigation system (INS), is described. Results from a field test are presented.

Thereafter, an approach is studied for calibrating a low-cost inertial measurement unit (IMU), requiring no mechanical platform for the accelerometer calibration and only a simple rotating table for the gyro calibration. The performance of the calibration algorithm is compared with the Cramér-Rao bound for cases where a mechanical platform is used to rotate the IMU into different precisely controlled orientations.

Finally, the effects of time synchronization errors in a GNSS-aided INS are studied in terms of the increased error covariance of the state vector. Expressions for evaluating the error covariance of the navigation state vector are derived. Two different cases are studied in some detail. The first considers a navigation system in which the timing error is not taken into account by the integration filter. This leads to a system with an increased error covariance and a bias in the estimated forward acceleration. In the second case, a parameterization of the timing error is included as part of the estimation problem in the data integration. The estimated timing error is fed back to control an adjustable fractional delay filter, synchronizing the IMU and GNSS-receiver data.

Place, publisher, year, edition, pages
Stockholm: KTH, 2007. vii,[7] p.
Series
Trita-EE, ISSN 1653-5146 ; 2007:066
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-4556 (URN)
Presentation
2007-12-07, Hörsal E32, KTH, Lindstedtsvägen 3, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20101117Available from: 2007-11-25 Created: 2007-11-25 Last updated: 2012-03-28Bibliographically approved

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Skog, IsaacHändel, Peter

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