Database correlation method for multi-system location: Master's thesis

    Research output: ThesisMaster's thesisTheses

    Abstract

    The future of the mobile positioning looks bright regardless of the fact that the growth in the markets of this field hasn’t been as good as it was anticipated to be a couple of years ago. A large variety of applications from friend finder services to vehicle navigation systems set different kinds of performance requirements for the applied positioning technique. Positioning accuracy is one of the most important performance measures. The accuracy of the satellite positioning techniques is generally better compared to the accuracy achieved with cellular network based positioning methods. In the near future, the
    difference might even increase due to the introduction of the Assisted GPS (A-GPS) and Galileo satellite navigation system. In spite of this, the development and utilization of the cellular network based positioning methods can be justified. That is, because the satellite positioning requires special mobile terminals and it might take still several years before these terminals become popular. On the other hand, satellite positioning cannot be utilized everywhere.
    The purpose of this work is to examine and enhance the Database Correlation Method (DCM) developed earlier at VTT. The enhancements consist of penalty term improvement, multi-system location and post-processing techniques including Kalman filter and Map-Matching algorithm. The theory including an overview of mobile positioning, the previous work related to database correlation and the introduction to the post-processing techniques is presented. Field measurements are carried out to collect terminal measurement data from real operating GSM and UMTS networks, and from WLAN indoor measurement campaign. These measurements are used to evaluate the implemented algorithms. The results of the performed positioning trials show that the enhancements do improve the positioning accuracy of the DCM algorithm. In conclusion, the DCM provides positioning accuracy suitable for many applications.
    Original languageEnglish
    QualificationMaster Degree
    Awarding Institution
    • Helsinki University of Technology
    Place of PublicationEspoo
    Publisher
    Publication statusPublished - 2005
    MoE publication typeG2 Master's thesis, polytechnic Master's thesis

    Fingerprint

    Correlation methods
    Satellites
    Navigation systems
    3G mobile communication systems
    Global system for mobile communications
    Processing
    Wireless local area networks (WLAN)
    Kalman filters
    Global positioning system

    Keywords

    • mobile positioning
    • database correlation
    • indoor positioning
    • Kalman filter
    • Map-Matching

    Cite this

    Kemppi, Paul. / Database correlation method for multi-system location : Master's thesis. Espoo : Helsinki University of Technology, 2005. 86 p.
    @phdthesis{cd60570cabca41ce8bcf67fc63188b54,
    title = "Database correlation method for multi-system location: Master's thesis",
    abstract = "The future of the mobile positioning looks bright regardless of the fact that the growth in the markets of this field hasn’t been as good as it was anticipated to be a couple of years ago. A large variety of applications from friend finder services to vehicle navigation systems set different kinds of performance requirements for the applied positioning technique. Positioning accuracy is one of the most important performance measures. The accuracy of the satellite positioning techniques is generally better compared to the accuracy achieved with cellular network based positioning methods. In the near future, thedifference might even increase due to the introduction of the Assisted GPS (A-GPS) and Galileo satellite navigation system. In spite of this, the development and utilization of the cellular network based positioning methods can be justified. That is, because the satellite positioning requires special mobile terminals and it might take still several years before these terminals become popular. On the other hand, satellite positioning cannot be utilized everywhere.The purpose of this work is to examine and enhance the Database Correlation Method (DCM) developed earlier at VTT. The enhancements consist of penalty term improvement, multi-system location and post-processing techniques including Kalman filter and Map-Matching algorithm. The theory including an overview of mobile positioning, the previous work related to database correlation and the introduction to the post-processing techniques is presented. Field measurements are carried out to collect terminal measurement data from real operating GSM and UMTS networks, and from WLAN indoor measurement campaign. These measurements are used to evaluate the implemented algorithms. The results of the performed positioning trials show that the enhancements do improve the positioning accuracy of the DCM algorithm. In conclusion, the DCM provides positioning accuracy suitable for many applications.",
    keywords = "mobile positioning, database correlation, indoor positioning, Kalman filter, Map-Matching",
    author = "Paul Kemppi",
    note = "CA: TTE Diplomity{\"o} Helsinki University of Technology, Department of Electrical and COmmunications Engineering",
    year = "2005",
    language = "English",
    publisher = "Helsinki University of Technology",
    address = "Finland",
    school = "Helsinki University of Technology",

    }

    Kemppi, P 2005, 'Database correlation method for multi-system location: Master's thesis', Master Degree, Helsinki University of Technology, Espoo.

    Database correlation method for multi-system location : Master's thesis. / Kemppi, Paul.

    Espoo : Helsinki University of Technology, 2005. 86 p.

    Research output: ThesisMaster's thesisTheses

    TY - THES

    T1 - Database correlation method for multi-system location

    T2 - Master's thesis

    AU - Kemppi, Paul

    N1 - CA: TTE Diplomityö Helsinki University of Technology, Department of Electrical and COmmunications Engineering

    PY - 2005

    Y1 - 2005

    N2 - The future of the mobile positioning looks bright regardless of the fact that the growth in the markets of this field hasn’t been as good as it was anticipated to be a couple of years ago. A large variety of applications from friend finder services to vehicle navigation systems set different kinds of performance requirements for the applied positioning technique. Positioning accuracy is one of the most important performance measures. The accuracy of the satellite positioning techniques is generally better compared to the accuracy achieved with cellular network based positioning methods. In the near future, thedifference might even increase due to the introduction of the Assisted GPS (A-GPS) and Galileo satellite navigation system. In spite of this, the development and utilization of the cellular network based positioning methods can be justified. That is, because the satellite positioning requires special mobile terminals and it might take still several years before these terminals become popular. On the other hand, satellite positioning cannot be utilized everywhere.The purpose of this work is to examine and enhance the Database Correlation Method (DCM) developed earlier at VTT. The enhancements consist of penalty term improvement, multi-system location and post-processing techniques including Kalman filter and Map-Matching algorithm. The theory including an overview of mobile positioning, the previous work related to database correlation and the introduction to the post-processing techniques is presented. Field measurements are carried out to collect terminal measurement data from real operating GSM and UMTS networks, and from WLAN indoor measurement campaign. These measurements are used to evaluate the implemented algorithms. The results of the performed positioning trials show that the enhancements do improve the positioning accuracy of the DCM algorithm. In conclusion, the DCM provides positioning accuracy suitable for many applications.

    AB - The future of the mobile positioning looks bright regardless of the fact that the growth in the markets of this field hasn’t been as good as it was anticipated to be a couple of years ago. A large variety of applications from friend finder services to vehicle navigation systems set different kinds of performance requirements for the applied positioning technique. Positioning accuracy is one of the most important performance measures. The accuracy of the satellite positioning techniques is generally better compared to the accuracy achieved with cellular network based positioning methods. In the near future, thedifference might even increase due to the introduction of the Assisted GPS (A-GPS) and Galileo satellite navigation system. In spite of this, the development and utilization of the cellular network based positioning methods can be justified. That is, because the satellite positioning requires special mobile terminals and it might take still several years before these terminals become popular. On the other hand, satellite positioning cannot be utilized everywhere.The purpose of this work is to examine and enhance the Database Correlation Method (DCM) developed earlier at VTT. The enhancements consist of penalty term improvement, multi-system location and post-processing techniques including Kalman filter and Map-Matching algorithm. The theory including an overview of mobile positioning, the previous work related to database correlation and the introduction to the post-processing techniques is presented. Field measurements are carried out to collect terminal measurement data from real operating GSM and UMTS networks, and from WLAN indoor measurement campaign. These measurements are used to evaluate the implemented algorithms. The results of the performed positioning trials show that the enhancements do improve the positioning accuracy of the DCM algorithm. In conclusion, the DCM provides positioning accuracy suitable for many applications.

    KW - mobile positioning

    KW - database correlation

    KW - indoor positioning

    KW - Kalman filter

    KW - Map-Matching

    M3 - Master's thesis

    PB - Helsinki University of Technology

    CY - Espoo

    ER -

    Kemppi P. Database correlation method for multi-system location: Master's thesis. Espoo: Helsinki University of Technology, 2005. 86 p.