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Sensor Fusion of GPS and speed information for low-cost automotive positioning and navigation
KTH, School of Electrical Engineering (EES), Signal Processing.
2011 (English)Student paper other, 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Global navigation satellite systems, such as the Global Positioning System (GPS) are nowadays widespread in the consumer and professional fields. To guarantee the desired accuracy, availability, integrity and robustness performance, it is necessary to add to GPS receivers aiding systems, like extra sensors, which can be expensive. In order to reduce the expenses, a low-cost alternative aiding system is here presented. The main idea is to extract the information needed to support the GPS services from an easily measurable signal as the one provided by the power supply of a car. This signal has a frequency component related to the rpm speed of the engine, thus it can be used to estimate the speed, and related states, when the GPS service is unavailable for some reason. Unfortunately, the frequency component isequal to the speed measured by the GPS up to a scale factor dependent on the gear engaged, so it is necessary to estimate over time these scale factors in order to use the information. In this thesis project we implemented an off-line system which leads to the estimation over time of the scale factors. A sensor fusion solution has been used: training data consisting only of speed measurement provided by the GPS and measurements of the signal of interest are processed through a bench of five Kalman Filters (one for each gear) which leads to the estimation of the scale factors. Three measurement campaigns with three different cars have been conducted in order to collect an exhaustive amount of datasets necessary to calibrate and then validate the system.

Place, publisher, year, edition, pages
2011. , 62 p.
EES Examensarbete / Master Thesis, XR-EE-SB 2011:001
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-53767OAI: diva2:470897
Subject / course
Signal Processing
Available from: 2012-01-19 Created: 2011-12-30 Last updated: 2012-03-23Bibliographically approved

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