Development of a regional GPS-based ionospheric TEC model for South Africa

Opperman, Ben; Cilliers, Pierre J.; McKinnell, Lee-Anne; Haggard, R.

doi:10.1016/j.asr.2007.02.026

Cited by 60 publications

(33 citation statements)

References 7 publications

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“…Opperman [6] used adjusted spherical harmonic model (ASHA) for near real-time regional ionospheric TEC mapping over South Africa.…”

Section: Interpolatedmentioning

confidence: 99%

Extending Ionospheric Correction Coverage Area By Using A Neural Network Method

Kim¹,

Kim²

2016

International Journal of Aeronautical and Space Sciences

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The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which combines observed ionospheric delay with the environmental parameters, is proposed. Neural network and least square regression algorithms are developed to utilize the combined input data. The bi-harmonic spline method is also tested for comparison. The IGS ionosphere map data is used to simulate the delays and to compute the extrapolation error statistics. The neural network method outperforms the other methods and demonstrates a high extrapolation accuracy. In order to determine the directional characteristics, the estimation error is classified into four direction components. The South extrapolation area yields the largest estimation error followed by North area, which yields the second-largest error.

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“…Opperman [6] used adjusted spherical harmonic model (ASHA) for near real-time regional ionospheric TEC mapping over South Africa.…”

Section: Interpolatedmentioning

confidence: 99%

Extending Ionospheric Correction Coverage Area By Using A Neural Network Method

Kim¹,

Kim²

2016

International Journal of Aeronautical and Space Sciences

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“…It uses data from a local GPS receiver network and was chosen to estimate single station TEC results for comparative purposes with ionosonde measurements . Full details of the TEC derivation procedure from GPS observations using the ASHA algorithm (and its validation with different data sources) are presented in Opperman et al (2007) andOpperman (2007).…”

Section: Tec From Gpsmentioning

confidence: 99%

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

Habarulema

McKinnell

2012

Ann. Geophys.

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Abstract. In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38 • S, 20.81 • E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95 • S, 18.47 • E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.

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“…Data for all satellites visible from only the GPS receiver at Sutherland were used in the interpolation. The slant to vertical TEC mapping function which was applied at each ionospheric pierce point (IPP) prior to interpolation and the adjusted spherical harmonic interpolation algorithm are described in [Opperman et al, 2007]. The VTEC at 12h00 SAST for each day was extracted from the resulting 24 h diurnal TEC interpolation [Habarulema et al, 2007], and is shown in Figure 4 as the target VTEC, henceforth referred to as the daily GPS TEC.…”

Section: Target Outputmentioning

confidence: 99%

“…[3] Other severely affected systems range from navigational systems (including high-frequency direction finding and satellite navigation) to Global Positioning System (GPS) surveying and space weather forecasts Hoffman-Wellenhof et al, 1992;Tulunay et al, 2004Tulunay et al, , 2006Opperman et al, 2007]. The vertical total electron content (TEC), defined as the number of free electrons in the ionosphere contained in a vertical column of unit cross sectional area (1 m 2 ), provides an indication of the ionospheric electron density and is often used to (partially) characterize the ionospheric variability.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear dynamic systems modeling using Gaussian processes: Predicting ionospheric total electron content over South Africa

Ackermann

Villiers

Cilliers³

2011

J. Geophys. Res.

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[1] Two different implementations of Gaussian process (GP) models are proposed to estimate the vertical total electron content (TEC) from dual frequency Global Positioning System (GPS) measurements. The model falseness of GP and neural network models are compared using daily GPS TEC data from Sutherland, South Africa, and it is shown that the proposed GP models exhibit superior model falseness. The GP approach has several advantages over previously developed neural network approaches, which include seamless incorporation of prior knowledge, a theoretically principled method for determining the much smaller number of free model parameters, the provision of estimates of the model uncertainty, and a more intuitive interpretability of the model.

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Development of a regional GPS-based ionospheric TEC model for South Africa

Cited by 60 publications

References 7 publications

Extending Ionospheric Correction Coverage Area By Using A Neural Network Method

Extending Ionospheric Correction Coverage Area By Using A Neural Network Method

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

Nonlinear dynamic systems modeling using Gaussian processes: Predicting ionospheric total electron content over South Africa

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