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In-Car Positioning and Navigation Technologies: a survey
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2718-0262
2009 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 10, no 1, 4-21 p.Article, review/survey (Refereed) Published
Abstract [en]

In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.

Place, publisher, year, edition, pages
2009. Vol. 10, no 1, 4-21 p.
Keyword [en]
Dead reckoning (DR), Inertial navigation, Information fusion, Satellite navigation, Vehicle models, Vehicle navigation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-7724DOI: 10.1109/TITS.2008.2011712ISI: 000263919800002Scopus ID: 2-s2.0-61849182591OAI: oai:DiVA.org:kth-7724DiVA: diva2:12835
Note
Uppdaterad från submitted till published(20101116) QC 20101116Available from: 2007-11-25 Created: 2007-11-25 Last updated: 2011-11-15Bibliographically 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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