Machine learning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations

Mallika, Lakshmi; Ratnam, D. Venkata; Raman, Saravana; Sivavaraprasad, G.

doi:10.1016/j.actaastro.2020.04.048

Cited by 31 publications

(13 citation statements)

References 18 publications

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“…A review of the techniques for modeling the total electron content of the ionosphere can be found in Bust & Mitchell (2008). New developments are still ongoing (see for example Bidaine & Warnant (2010); Ansari et al (2017); Li et al (2020b); Yasyukevich et al (2015Yasyukevich et al ( , 2020Yasyukevich et al ( , 2022; Li et al (2021); Pudlovskiy (2021)); notably machine learning techniques have developed in this field like many others (Orus Perez, 2019;Mallika I et al, 2020;Ferreira et al, 2017). Typical examples of tomography reconstruction algorithm of the ionosphere in the literature, such as Seemala et al (2014), model the TEC over wide timescales (more than an hour, possibly more than a half day) and wide geographical regions (with cells hundreds of kilometers wide).…”

Section: The Ionospherementioning

confidence: 99%

A simple ionospheric correction method for radar-based space surveillance systems, with performance assessment on GRAVES data

Herscovici-Schiller,

Gachet,

Couetdic

et al. 2022

Preprint

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Ionospheric effects degrade the quality of radar data, which are critical for the precision of the satellite ephemeris produced by space surveillance systems; this degradation is especially noticeable for radars such as GRAVES that operate in the very high frequency range. This article presents a simple and effective method to correct for ionospheric effects, with an evaluation on data obtained with GRAVES, the French space surveillance radar. This method relies on GPS data, and our evaluation relies on GRAVES and DORIS data. We found that the gain in terms of evaluated radial velocity can be as high as 1.76κ, where κ is the typical root mean square of the noise on radial velocity measurements for GRAVES (excluding ionospheric effects): the error decreases from 2.60κ to 0.83κ for daytime satellite overhead passes. Our conclusion is that, while this method is very simple to implement, it has proven to be a good correction for ionospheric effects in practice.

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Section: The Ionospherementioning

confidence: 99%

A simple ionospheric correction method for radar-based space surveillance systems, with performance assessment on GRAVES data

Herscovici-Schiller,

Gachet,

Couetdic

et al. 2022

Preprint

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“…It is also worth to mention that the mathematical background of the GPR and popular Kriging in geostatistics are the same Stein (1999). Data from a single station in India was used to forecast ionospheric delay using GPR technique with almost 100% accuracy Lakshmi et al (2020). Another GPR approach was used to predict daily TEC values based upon the TEC values recorded at various permanent GNSS station in Turkey Inyurt et al (2020).…”

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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“…As a result, its dependability and performance are critical for various systems. The state of the ionosphere and its amount of influence by solar radiation and the geomagnetic field is one of the factors that affect GNSS radio communication transmissions [1].…”

Section: Introductionmentioning

confidence: 99%

TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

Shakir¹,

Othman²,

Bakar³

2022

IJACSA

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Forecasting the ionosphere layer's total electronic content (TEC) is crucial for its impact on satellite signals and global positioning systems (GPS) and the ability to predict earthquakes. The existing statistical-based forecasting models such as ARMA, ARIMA, and HW suffered from the TEC nonstationarity nature, which requires algorithmic handling of the forecasting and the mathematical part. This study proposes a hybrid method that incorporates several components and is designated as Optimized Variational Mode Decomposition with Recursive Neural Network Forecasting (OVMD-RNN) to forecast TEC. Before using the Elman Network to train each component, Variational Mode Decomposition (VMD) was used to decompose the signal into its essential stationary components. In addition, the proposed method includes an optimization algorithm for determining the best VMD decomposer parameters. The GPS Ionospheric Scintillation and TEC Monitor (GISTM) at Universiti Kebangsaan Malaysia station have been used to evaluate the method based on collected datasets for three years, 2011, 2012, and 2013. The experiment findings show that the model has successfully tracked all the up and down patterns in the time series. The results also reveal that VMD-based training might not always provide good results due to the residual signal. Finally, the evaluation focused on generating loss value and comparing it to the ARIMA benchmark. It showed that OVMD-RNN had accomplished a maximum improvement percentage of ARIMA with a value of (99%).

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Machine learning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations

Cited by 31 publications

References 18 publications

A simple ionospheric correction method for radar-based space surveillance systems, with performance assessment on GRAVES data

A simple ionospheric correction method for radar-based space surveillance systems, with performance assessment on GRAVES data

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

TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

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