Accurate ionospheric delay model for real-time GPS-based positioning of LEO satellites using horizontal VTEC gradient estimation

Renga, Alfredo; Causa, Flavia; Tancredi, Urbano; Grassi, Michele

doi:10.1007/s10291-018-0710-5

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…The real-time vTEC estima-tion was considered in Huang and Reinisch [2001]. In Renga et al [2018], the authors proposed a Linear Thin Shell model to better describe the horizontal variation of the vTEC distribution in real-time. Several researches introduce multiple receivers to jointly estimate the vTEC.…”

Section: Related Workmentioning

confidence: 99%

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Lin

Song

Qian

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In this work, we consider a data-mining approach to enhance the GPS signal. We build a large-scale high precision GPS receiver grid system to collect real-time GPS signals for training. The Gaussian Process (GP) regression is chosen to model the vertical Total Electron Content (vTEC) distribution of the ionosphere of the Earth. Our experiments show that the noise in the real-time GPS signals often exceeds the breakdown point of the conventional robust regression methods resulting in sub-optimal system performance. We propose a three-step approach to address this challenge. In the first step we perform a set of signal validity tests to separate the signals into clean and dirty groups. In the second step, we train an initial model on the clean signals and then reweigting the dirty signals based on the residual error. A final model is retrained on both the clean signals and the reweighted dirty signals. In the theoretical analysis, we prove that the proposed three-step approach is able to tolerate much higher noise level than the vanilla robust regression methods if two reweighting rules are followed. We validate the superiority of the proposed method in our real-time high precision positioning system

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Section: Related Workmentioning

confidence: 99%

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Lin

Song

Qian

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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show abstract

“…The commonly accepted exchange format is the IONEX Schaer et al (1998), that represents the 2-and 3-dimensional TEC maps given in a geographic grid. Thanks to the rapid development of computer processing, providing global and regional tec maps can be done in near-real-time (Coster et al 1992;Bergeot et al 2014;Erdogan et al 2017;Chang et al 2019;Renga et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

Lupsic

Takács

2022

Acta Geod Geophys

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The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, the number of single-frequency receivers is still significant. Since their measurements are influenced by ionospheric delay, accurate ionosphere models are of utmost importance to reduce the effect. This paper summarizes how Gauss process regression (GPR) can be applied to derive near real-time regional ionosphere models using raw Global Navigation Satellite System (GNSS) observations of permanent stations. While Gauss process is widely used in machine learning, GPR is a nonparametric, Bayesian approach to regression. GPR has several benefits for ionosphere monitoring since it is quite robust and efficient to derive a grid model from data available in irregular set of ionospheric pierce points. The corresponding instrumental delays are estimated by a parallel Kalman filter. The presented algorithm can be applied near real-time, however the results are offline calculated and are compared to two high quality TEC map products. Based on the analysis, the accuracy of the GPR modell is in 2 TECu range. The developed methods could be efficiently applied in the field of autonomous vehicle navigation with meeting both accuracy and integrity requirements.

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“…There have been some studies on modeling and forecasting ionospheric delays [7,[18][19][20], making it is possible to incorporate an ionosphere forecasting model in cycle slip detections and repairs. Zhao et al [21] sequentially determined cycle slips on the extra-wide lane (EWL), wide lane (WL), and narrow lane (NL) in three cascading steps.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Cycle Slip Detection and Repair Method Based on Ionospheric Delay Prediction for Undifferenced Triple-Frequency BDS Signals

et al. 2021

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The detection and repair of cycle slips are key steps in high-accuracy GNSS (Global Navigation Satellite System) data processing using carrier phase observations. BDS (BeiDou Navigation Satellite System) triple-frequency observations provide better combinations for cycle slip detections and repairs compared to dual-frequency observations. Although a number of algorithms have been developed and may correctly detect cycle slips most of the time, the reliability of empirical thresholds methods cannot be guaranteed. In this study, an adaptive threshold is proposed for three sets of triple-frequency Geometry-Free (GF) pseudorange minus phase combinations to improve the cycle slip detection performance and reduce the false alarm rate of the cycle slip detection by combining the predicted epoch-differenced ionospheric delays under active ionospheric conditions. Moreover, in the cycle slip repair, the integral combined cycle slips are determined by directly rounding the estimated float-combined cycle slips, which will lead to a repair error if the between-epoch ionospheric variation is large. In this study, a new rounding method considering the predicted epoch-differenced ionospheric delays is proposed, and it is proven that the new method has a higher success rate for estimating the integer value of a cycle slip than the traditional method. The performance of the newly proposed method is validated by using static BDS triple-frequency observations that contain simulated cycle slips. BDS triple-frequency observations were collected at 30-s sampling intervals under active ionospheric conditions. The results show that this method can successfully detect and repair all slips of more than one cycle. In addition, dynamic BDS data collected with a vehiclebased receiver at a 1-s sampling intervals are processed, and the results show that the proposed method is also effective in the detection and repair of cycle slips in dynamic data.

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Accurate ionospheric delay model for real-time GPS-based positioning of LEO satellites using horizontal VTEC gradient estimation

Cited by 12 publications

References 36 publications

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

A Real-Time Cycle Slip Detection and Repair Method Based on Ionospheric Delay Prediction for Undifferenced Triple-Frequency BDS Signals

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