Characterizing Rigging Crew Proximity to Hazards on Cable Logging Operations Using GNSS-RF: Effect of GNSS Positioning Error on Worker Safety Status

Global Navigation Satellite System (GNSS)-based applications rely on the quality of the GNSS position, navigation, and timing (PNT) services, accomplished through measurement and processing of satellite signals propagation characteristics in a process commonly known as satellite navigation. GNSS positioning performance is in the foundation of the quality of service of GNSS-based applications including the growing number of them in transport, traffic and Intelligent Transport Systems segments, thus a need for a common and independent approach. Here, we propose a novel method for the assessment of the contribution of a single cause to the over-all GNSS positioning error. Proposed method is demonstrated in the case of the GNSS multipath effects, resulting with the experimental predictive model of the direct multipath contribution to GNSS positioning error. The predictive models developed in this research is aimed at deployment in the GNSS positioning performance assessment for GNSS-based applications in transport and telecommunications.

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Expression of GNSS Positioning Error in Terms of Distance

PROMET - Traffic&Transportation ◽

10.7307/ptt.v30i3.2304 ◽

2018 ◽

Vol 30 (3) ◽

pp. 305-310

Author(s):

Mia Filić ◽

Renato Filjar ◽

Marko Ševrović

Keyword(s):

Coordinate System ◽

Performance Assessment ◽

Reference Frame ◽

Software Defined Radio ◽

Data Access ◽

Positioning Error ◽

Rectangular Coordinate System ◽

Distance Method ◽

Spherical Coordinate ◽

Gnss Positioning

This manuscript analyzes two methods for Global Navigation Satellite System positioning error determination for positioning performance assessment by calculation of the distance between the observed and the true positions: one using the Cartesian 3D rectangular coordinate system, and the other using the spherical coordinate system, the Cartesian reference frame distance method, and haversine formula for distance calculation. The study shows unresolved issues in the utilization of position estimates in geographical reference frame for GNSS positioning performance assessment. Those lead to a recommendation for GNSS positioning performance assessment based on original WGS84-based GNSS position estimates taken from recently introduced data access from GNSS software-defined radio (SDR) receivers.

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GNSS positioning error forecasting in the Arctic: ROTI and Precise Point Positioning error forecasting from solar wind measurements

Journal of Space Weather and Space Climate ◽

10.1051/swsc/2021024 ◽

2021 ◽

Author(s):

vincent fabbro ◽

Knut Stanley Jacobsen ◽

Yngvild Linnea Andalsvik ◽

Sebastien Rougerie

Keyword(s):

Solar Wind ◽

Precise Point Positioning ◽

Arctic Region ◽

The Arctic ◽

Error Estimator ◽

Positioning Error ◽

Disturbance Index ◽

Gnss Positioning ◽

Point Positioning ◽

Index Level

A model forecasting ionospheric disturbances and its impact on GNSS positioning is proposed, called HAPEE (High lAtitude disturbances Positioning Error Estimator). It allows predicting ROTI index and corresponding Precise Point Positioning (PPP) error in Arctic region (i.e. latitudes > 50° ). The model is forecasting for the next hour a probability of a disturbance index or PPP error to exceed a given threshold, from solar wind conditions measured at L1 Lagrange point. Or alternatively, it is forecasting a disturbance index level that is exceeded during the next hour for a given percentage of the time. The ROTI model has been derived from NMA network measurements, considering a database covering the years 2007 up to 2019. It is demonstrated that the statistical variability of the ROTI index is mainly following a lognormal distribution. The proposed model has been tested favorably on measurements performed using measurements from stations of the NMA network that were not used for the model derivation. It is also shown that the statistics of PPP error conditioned by ROTI is following a Laplace distribution. Then a new compound model has been proposed, based on a conditional probability combining ROTI distribution conditioned by solar wind conditions and error distributions conditioned by ROTI index level.

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