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Vehicular Positioning Using 5G and Sensor Fusion
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recent advances in the telecommunications industry and the resulting applicationssuch as autonomous vehicles, vehicle surveillance and traffic safetyhas increased the demand for accurate and robust vehicle positioning systems.Existing Global Navigation Satellite System (GNSS) based positioning techniquesface significant performance loss in the tunnels and urban canyons.Recent researches have shown that radio-based positioning techniques are theoreticallypromising to make an accurate navigation system to fill the GNSSgaps. Fifth generation of mobile communication (5G) will utilize wide bandwidthstogether with beamforming enabled by antenna arrays to provide higherdata rates to mobile users. These features make 5G a favorable candidate forhigh accuracy positioning. On the other hand, sensor fusion is commonly employedto develop more robust and accurate navigation systems for vehicles. Inthis work, the range and angle measurements from 5G base stations are fusedwith the acceleration measurements by the means of the extended Kalman filterto generate position estimates for a moving car. The accuracy of this positioningsystem is studied with centimeter wave (cmWave) and millimeter wave(mmWave) 5G cellular networks which are set up by practical parameters. Towardsthat, the positioning system is tested in a simulation-based experimentwhere a car is moving on a highway and the 5G base stations are deployedalongside of it. Based on that, a detailed analysis of the Kalman filter’s rootmean squared error (RMSE) and the 5G’s different parameters and limitingfactors such as the line of sight (LOS) blockage is carried out. Our numericalresults show that vehicles connected to 5G can benefit from this system to enhancethe robustness and accuracy of their navigation system.

Abstract [sv]

De senaste framstegen inom telekommunikationsindustrin och de resulterandeapplikationerna som autonoma fordon, fordonsövervakning och trafiksäkerhethar ökat efterfrågan på exakta fordonspositioneringssystem. ExisterandeGlobal Navigation Satellite System (GNSS) baserade positioneringsteknikerhar en betydande prestandaförlust i tunnlar och urbana kanjoner. Forskninghar visat att radiobaserade positioneringstekniker har mindre distributionskostnaderoch kan vara mer exakta än satellitbaserade navigationssystem.I den femte generation av mobilkommunikation (5G) används tekniker sommillimeterWave (mmWave) och multiple-input multiple-output (MIMO) därradio-terminaler består av stora matrisantenner och arbetar med stora bandbredder.Dessa funktioner gör 5G-system gynnsamma för positionering medhög noggrannhet. Å andra sidan har informationsfusion av Inertial NavigationSystems (INS) och andra positioneringstekniker vanligen använts för attutveckla mer robusta och exakta spårningssystem. I denna studie föreslår viett INS/5G-positioneringssystem för att spåra landfordon baserat på Kalmanfiltret. Vi adresserar systempositioneringsgränserna i termer av 5G nya radio(NR) subsystem och en detaljerad analys av beroendet av rotmedelfelteradkvadratfel (RMSE) för olika systemparametrar som utförs. Systemet testas iett enkelt simuleringsbaserat experiment som består av en rak motorväg medbasstationerna placerade bredvid det. Slutligen visar våra numeriska resultatatt det föreslagna systemet är i stånd att lokalisera ett UE-monterat fordon medsub-meter lägesfel även i närvaro av hård siktlinje blockering.

Place, publisher, year, edition, pages
2019. , p. 69
Series
TRITA-EECS-EX ; 2019:672
Keywords [en]
Positioning, 5G, Sensor Fusion, Beamforming, IMU, Kalman Filter
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-266117OAI: oai:DiVA.org:kth-266117DiVA, id: diva2:1381388
External cooperation
Ericsson
Supervisors
Examiners
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-29Bibliographically approved

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