Evaluating the Urban Trench Model for Improved Positioning in Urban Areas

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Icking, Lucy: Evaluating the Urban Trench Model for Improved Positioning in Urban Areas. Hannover : Gottfried Wilhelm Leibniz Universität, Institut für Erdmessung, Master Thesis, 2019, 78 S. DOI: https://doi.org/10.15488/10600

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Sum total of downloads: 325




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Abstract: 
Urban environments still form a challenge for Global Navigation Satellite System (GNSS) positioning due to its diffcult conditions for signal propagation. Many obstruction sources disturb the GNSS signal, signal loss or multipath are examples for effects occurring in these cases.This work focuses on an approach which distinguishes visible line-of-sight (LOS) satellite signals from obstructed non-line-of-sight (NLOS) signals. These are determined in a self-developed algorithm with the help of a 3D CityModel that is provided by the city of Hanover. For the evaluation, a kinematic experiment is conducted with geodetic and high-sensitivity receivers in a repeated trajectory. Further sensors were used to generate a reference trajectory.Open questions not only concern the signal characterization with respect to LOS/NLOS properties but also to what extent NLOS observations can be used in a constructive sense. Therefore, reflection points of NLOS signals are calculated, examined and introduced in a Single Point Positioning (SPP) solution.Additionally, these issues are put in the context of different receivers, each of which has different properties and hence diverse outcomes.Results show that the LOS status can be determined reliably but that the computed detour lengths not always match the Observed Minus Computed (OMC) values. Taking only those additional path lengths into account that fit the OMC, an improvement could be achieved in an SPP solution. Further findings show that OMC are especially high in street sections that have a perpendicular orientation compared to the satellite's azimuth angle.
License of this version: CC BY 3.0 DE
Document Type: MasterThesis
Publishing status: publishedVersion
Issue Date: 2019-09-11
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 164 50.46%
2 image of flag of United States United States 38 11.69%
3 image of flag of Russian Federation Russian Federation 32 9.85%
4 image of flag of Czech Republic Czech Republic 29 8.92%
5 image of flag of China China 18 5.54%
6 image of flag of France France 5 1.54%
7 image of flag of Hong Kong Hong Kong 4 1.23%
8 image of flag of No geo information available No geo information available 3 0.92%
9 image of flag of Lithuania Lithuania 3 0.92%
10 image of flag of United Kingdom United Kingdom 3 0.92%
    other countries 26 8.00%

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