A method and a model for risk assessment of GNSS utilisation with a proof-of-principle demonstration for polar GNSS maritime applications

The GNSS positioning performance is commonly defined and described in terms unspecified to particular GNSS-based application. The approach causes difficulties to GNSS application developers, operators, and users, rendering the impact assessment of GNSS performance on the GNSS application Quality of Service (QoS) particularly difficult. Here the Probability of Occurrence (PoO) Model is introduced, which allows for a risk assessment of the probability for the GNSS positioning accuracy failure to meet the requirements of the particular GNSS-based application. The proposed PoO Model development procedure requires a large set of position estimation errors observations, which shall cover a range of classes of positioning environment (space weather, troposphere, multi-path etc.) disturbances affecting GNSS positioning accuracy. As result, the PoO Model becomes a tool that returns the probability of failure in meeting the positioning accuracy requirements of the GNSS applications considered, thus providing the input for a GNSS deployment risk assessment. The proposed PoO Model and its development procedure are demonstrated in the case of polar region positioning environment, with raw GNSS pseudorange observations taken at the International GNSS Service (IGS) Network reference station Iqualuit, Canada are used for the PoO Model development. The PoO Model proof-of-principle is then used to estimate the probability of the unmet required positioning accuracy for a number of polar maritime navigation applications. Manuscript concludes with a discussion of the PoO Model benefits and shortcomings, a summary of contribution, and intentions for the future research

Analysis of tropospheric contribution to GPS positioning error during tropospheric cyclone Marcus in 2018

GNSS positioning performance assessment is essential for sustainable development of a growing number of GNSS-based technology and socio-economic applications. Case-studies of GNSS positioning performance in critical environments and applications scenarios reveals vulnerabilities of the GNSS Positioning, Navigation, and Timing (PNT) services, and suggest mitigation techniques and GNSS application risk containment. Here we address the case of GPS positioning performance during a devastating tropical cyclone Marcus that hit the greater area of the city of Darwin, Australia in 2018. We identified specific statistical properties of time series of tropospheric contribution to GPS northing, easting, and vertical positioning error that may contribute to understanding of tropospheric effects on GPS positioning performance during a massive weather deterioration in maritime and coastal areas, and analysed their adversarial effects on GNSS-based maritime applications

Modelling GPS positioning performance in Northwest Passage during extreme space weather conditions

New shipping routes are emerging as a result of iceberg melting in polar regions, allowing for more efficient transport of people and goods. Opening of the Northwest Passage, the maritime route connecting Pacific Ocean with Atlantic Ocean through Arctic region, is considered such a development. The increasing transport exploitation of the Northwest Passage requires the quality assessment of maritime navigation aids for compliance with the established requirements. Here we contribute to the subject with addressing the polar commercial-grade GPS positioning performance in the Northwest Passage in the extreme positioning environment conditions during the massive 2003 space weather storm, a space weather event similar to the Carrington Storm of 1859, the largest space weather event recorded. The GPS positioning environment in the Northwest Passage during the Carrington-like storm in 2003 was reconstructed through the GNSS SDR receiver-post processing of the experimental GPS observations. The raw GPS dual-frequency pseudoranges and navigation messages were collected at the International GNSS Service (IGS) reference station at Ulukhaktok, Victoria Island, Canada. Pseudorange processing and GPS position estimation were performed in three scenarios of pre-mitigation of the ionospheric effects, known as the single major contributor GPS positioning error: (i) no corrections applied, (ii) Klobuchar-based corrected GPS positioning, and (iii) dual-frequency corrected GPS positioning. Resulting GPS positioning error vectors were derived as positioning error residuals from the known reference station position. Statistical properties of the northing, easting, and vertical components of the GPS positioning error vector were analyzed with a software developed in the R environment for statistical computing to select suitable methods for the GPS positioning error prediction model development. The analysis also identified the most suitable theoretical fit for experimental statistical distributions to assist the model development. Finally, two competitive GPS positioning error prediction models were developed, based on the exponential smoothing (reference) and the generalized regression neural networks (GRNN) (alternative) methods. Their properties were assessed to recommend their use as mitigation methods for adverse massive space weather effects in polar regions