2019
DOI: 10.1002/navi.276
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Gaussian‐Pareto overbounding of DGNSS pseudoranges from CORS

Abstract: This paper presents a novel approach for overbounding unknown distribution functions called the Gaussian-Pareto overbounding. This extends the current practice of using Gaussian distributions for overbounding, but combines it with methods from Extreme Value Theory for modeling tails. Hence, this approach uses a Gaussian distribution to overbound the core of the distribution and generalized Pareto distributions for the tails. Furthermore, this approach is applied to Differential Global Navigation Satellite Syst… Show more

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Cited by 14 publications
(14 citation statements)
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“…Non-Gaussian methods lead to tight overbounds, revealing the trend of the entire underlying distribution. At present, instances of such approaches include the stable overbound [26] and the Gaussian-Pareto overbound (GPO) [27]. Unfortunately, these two methods have defects in their mathematical properties.…”
Section: Previous Work On Single-frequency Overboundsmentioning
confidence: 99%
“…Non-Gaussian methods lead to tight overbounds, revealing the trend of the entire underlying distribution. At present, instances of such approaches include the stable overbound [26] and the Gaussian-Pareto overbound (GPO) [27]. Unfortunately, these two methods have defects in their mathematical properties.…”
Section: Previous Work On Single-frequency Overboundsmentioning
confidence: 99%
“…1 remains a bound-in other words, the probability of the actual VPE exceeding VPL H0 is no greater than P ffmd . Note that, while standard models for σ pr_gnd n exist (e.g., see RTCA 2004), they are not guaranteed to sufficiently bound actual errors for every ground station installation, so this must be validated with specialized analysis and data (Blanch et al 2019;Larson et al 2019).…”
Section: Vertical Protection Level Computationmentioning
confidence: 99%
“…Future work will also explore the application of our overbounding methods to SDM and broader GNSS data sets. A forthcoming study combines our overbounding methods with extreme value theory to perform a statistical characterization of distribution far tails .…”
Section: Future Workmentioning
confidence: 99%