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Record W7048123613

Ionospheric scintillation sensitive GNSS tracking error models and mitigation approaches

2021· other· en· W7048123613 on OpenAlexaboutno aff

Bibliographic record

VenueNottingham ePrints (University of Nottingham) · 2021
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsGNSS applicationsScintillationInterplanetary scintillationSatellite systemGNSS augmentationSatelliteSatellite navigation
DOInot available

Abstract

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Ionospheric scintillation refers to the rapid and random fluctuations in intensity and phase of radio frequency signals when they propagate through plasma density irregularities in the ionosphere. It is more frequently observed in the auroral to polar regions and the equatorial to low latitude regions. When scintillation occurs on Global Navigation Satellite System (GNSS), the GNSS signal quality and receiver performance can be significantly degraded, thus increasing the errors in positioning and navigation. Under strong scintillation, the GNSS receiver can even lose the lock on the signals, posing serious threats to safety-critical GNSS applications and precise positioning. \nFor a better understanding of scintillation effects on GNSS signals and receivers, as well as to mitigate the scintillation effects on GNSS positioning, research is carried out in this thesis focusing on the following three aspects: (1) characterizing the GNSS signal intensity fadings under scintillation, (2) modelling scintillation effects on GNSS receiver tracking loops and (3) developing scintillation mitigation approaches to support high accuracy GNSS positioning under scintillation. \nSignal intensity fadings is one of the reasons that degrade the GNSS receiver tracking performance. By exploiting three months of raw scintillation data recorded by an ionospheric scintillation monitoring receiver (ISMR) deployed at low latitudes, signal intensity fadings due to scintillation are detected and characterized. Their effects on receiver tracking performance are analysed, which contributes to better understanding the low latitude scintillation effects on GNSS receivers. In order to quantitatively model the scintillation effects on GNSS receiver Phase Locked Loops (PLLs) and Delayed Locked Loop (DLLs), the phase and code jitter are estimated, respectively, at the output of PLL and DLL, taking scintillation effects into consideration. The existing models to estimate the phase and code jitters are studied. To address the concerns of the existing models, an alternative approach is developed to estimate the phase and code jitter under scintillation using the output of tracking loop discriminators, which better reflects the actual PLL and DLL tracking performance under scintillation. Additionally, the distribution of the tracking errors are analysed in the presence of scintillation. A customer-defined probability density function is proposed for the first time, which successfully describes the distribution of the PLL tracking errors under different levels of scintillation. \nThe approach to mitigate scintillation effects on GNSS positioning is studied. This thesis employs a phase and code jitter weighting approach to reduce the positioning errors caused by scintillation. In this approach, the positioning stochastic models are improved using the estimated phase and code jitter values considering scintillation effects. In order to improve the performance of this approach, 1-second scintillation indices are proposed in this thesis, which shows more effectiveness in describing the signal fluctuations under scintillation compared with the widely used 1-minute scintillation indices. Additionally, the 1-second scintillation indices outperform the 1-minute ones when used in mitigating positioning errors under scintillation. To implement the scintillation mitigation approach on generic receivers, which are not able to estimate the scintillation indices and consequently the phase and code jitter, the concept of phase and code jitter maps is exploited in this thesis. In this way, generic receivers can extract and calculate the jitter values directly from these maps for each measurement. Regional phase and code jitter maps are constructed in northern Canada using the scintillation data recorded during the geomagnetic storm in September 2017. Results show that with the help of the jitter maps constructed in this thesis, the positioning accuracy at both the ISMR and generic receiver stations can be greatly improved under scintillation.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.241
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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