Novel Methods for Personal Indoor Positioning

    Research output: Book/ReportDoctoral thesisMonograph

    761 Downloads (Pure)


    Currently, people are used to getting accurate GNSS based positioning services. However, in indoor environments, the GNSS cannot provide the accuracy and availability comparable to open outdoor environments. Therefore, alternatives to GNSS are needed for indoor positioning. In this thesis, methods for pedestrian indoor positioning are proposed. With these novel methods, the mobile unit performs all the required positioning measurements and no dedicated positioning infrastructure is required.

    This thesis proposes novel radio map configuration methods for WLAN fingerprinting based on received signal strength measurements. These methods with different model parameters were studied in field tests to identify the best models with reasonable positioning accuracy and moderate memory requirements. A histogram based WLAN fingerprinting model is proposed to aid IMU based pedestrian dead reckoning that is obtained using a gyro and a 3-axis accelerometer, both based on MEMS technology. The sensor data is used to detect the steps taken by a person on foot and to estimate the step length and the heading change during each step.

    For the aiding of the PDR with WLAN positioning, this thesis proposes two different configurations of complementary extended Kalman filters. The field tests show that these configurations produce equivalent position estimates. Two particle filters are proposed to implement the map aided PDR: one filter uses only the PDR and map information, while the other uses also the WLAN positioning. Based on the field tests, map aiding improves the positioning accuracy more than WLAN positioning.

    Novel map checking algorithms based on the sequential re-selection of obstacle lines are proposed to decrease the computation time required by the indoor map matching. To present the map information, both unstructured and structured obstacle maps are used. The feasibility of the proposed particle filter algorithms to real time navigation were demonstrated in field tests.
    Original languageEnglish
    PublisherTampere University of Technology
    Number of pages129
    ISBN (Electronic)978-952-15-3612-0
    ISBN (Print)978-952-15-3596-3
    Publication statusPublished - 27 Oct 2015
    Publication typeG4 Doctoral dissertation (monograph)

    Publication series

    NameTampere University of Technology. Publication
    PublisherTampere University of Technology
    ISSN (Print)1459-2045


    Dive into the research topics of 'Novel Methods for Personal Indoor Positioning'. Together they form a unique fingerprint.

    Cite this