TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Error Detection in Personal Satellite Navigation

Tutkimustuotos

Yksityiskohdat

AlkuperäiskieliEnglanti
KustantajaTampere University of Technology
Sivumäärä85
ISBN (elektroninen)952-15-1837-5
ISBN (painettu)952-15-1670-4
TilaJulkaistu - 1 joulukuuta 2006
OKM-julkaisutyyppiG5 Artikkeliväitöskirja

Julkaisusarja

NimiTampere University of Technology. Publication
KustantajaTampere University of Technology
Vuosikerta629
ISSN (painettu)1459-2045

Tiivistelmä

Personal positioning repeatedly occurs in severely degraded signal conditions, which sets a challenge for all error detection methods. Compared to ideal positioning conditions, the average signal condition is weaker and every tracked signal is more invaluable, simultaneously. Therefore, discarding a signal is a non-favored decision which is also often difficult to make as the combination of signal condition and satellite geometry is complex. Expert decision-making is required when the satellite subset is selected for positioning.This thesis proposes new methods for error detection in satellite navigation, and aims to serve as an up-to-date survey of existing methods. The focus of the thesis being in personal positioning, another objective is to find ways to utilize possible cellular connection in error detection. New methods outside the traditional family of fault detection algorithms, which are based on data self-redundancy tests, are presented. After representing the required preliminaries about satellite positioning, the thesis continues by introducing satellite signal condition analysis and environment detection analysis, which both employ probabilistic reasoning methods, including Dempster-Shafer theory. Then, the weighted satellite geometry measure, KDOP, and the error detection method based on that, are presented, and the essential feature of non-monotonicity of KDOP is addressed. This is followed by a consideration on the utilization of cellular network in the perspective of coarse integrity monitoring and reference position delivery. All the implemented algorithms were tested with real satellite navigation (and cellular) data as batch processing.According to the obtained results, the proposed methods succeed in bringing new information about the positioning conditions to support different decision-making tasks of the receiver, and they are suitable for error detection. The approach of the KDOP method presents novelty by combining the subset satellite geometry and signal condition factors into one quality parameter of a position estimate. The presented method of cellular position databases supports error detection task in a complementary manner utilizing cellular connection of a GNSS receiver.

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