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    On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact

    Access Status
    Fulltext not available
    Authors
    Khodabandeh, A.
    Wang, J.
    Rizos, C.
    El-Mowafy, Ahmed
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Khodabandeh, A. and Wang, J. and Rizos, C. and El-Mowafy, A. 2019. On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact. GPS Solutions. 23 (3): UNSP 73.
    Source Title
    GPS Solutions
    DOI
    10.1007/s10291-019-0863-x
    ISSN
    1080-5370
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    URI
    http://hdl.handle.net/20.500.11937/75694
    Collection
    • Curtin Research Publications
    Abstract

    © 2019, Springer-Verlag GmbH Germany, part of Springer Nature. High-precision single-receiver positioning requires the provision of reliable network-derived corrections. Care must therefore be exercised to continuously check the quality of the corrections and to detect the possible presence of mis-modeled biases in the network data. In network-RTK or its state-space implementation, PPP-RTK, quality control of the solutions is executed in two separate phases: the network component and the user component. Once confidence in the network-derived solutions is declared, a subset of the solutions is sent as corrections to a single-receiver user, thereby allowing the user to separately check the integrity of his network-aided model. In such a two-step integrity monitoring procedure, an intermediate step is missing, the integrity monitoring of the corrections themselves. It is the goal of this contribution to provide a quality control procedure for GNSS parameter solutions at the correction level, and to measure the impact a missed detection bias has on the (ambiguity-resolved) user position. New detection test statistics are derived with which the single-receiver user can check the overall validity of the corrections even before applying them to his data. A small-scale network of receivers is utilized to provide numerical insights into the detectability of mis-modeled biases using the proposed detectors and to analyze the impact of such biases on the user positioning performance.

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