Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data

Jayapal, V.; Zain, Ahmad Faizal Mohd

doi:10.1109/piers.2016.7734425

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…In this study, a comparative analysis of four interpolation methods is performed. These methods include nearest-neighbor extrapolation [36], bilinear interpolation [18], Junkins weighted interpolation, and bicubic interpolation. The principles of the Junkins weighted and bicubic interpolation methods are described below.…”

Section: Different Interpolation Methods For Gim Productsmentioning

confidence: 99%

Research on Accelerating Single-Frequency Precise Point Positioning Convergence with Atmospheric Constraint

et al. 2019

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An increasing number of researchers have conducted in-depth research on the advantages of low-cost single-frequency (SF) receivers, which can effectively use ionospheric information when compared to dual-frequency ionospheric-free combination. However, SF observations are bound to increase the unknown parameters and prolong the convergence time. It is desirable if the convergence time can be reduced by external information constraints, for example atmospheric constraints, which include ionosphere-or troposphere constraints. In this study, ionospheric delay constraints, tropospheric delay constraints, and their dual constraints were considered. Additionally, a total of 18,720 test experiments were performed. First, the nearest-neighbor extrapolation (NENE), bilinear-(BILI), bicubic-(BICU), and Junkins weighted-interpolation (JUNK) method of Global Ionospheric Map (GIM) grid products were analyzed. The statistically verified BILI in the percentage of convergence time, average convergence time, and computation time consumption of them shows a good advantage. Next, the influences of global troposphere-and ionosphere-constrained on the convergence time of SF Precise Point Positioning (PPP) were analyzed. It is verified that the ionosphere-constrained (TIC2) has significant influence on the convergence time in the horizontal and vertical components, while the troposphere-constrained (TIC1) has better effect on the convergence time in the vertical components within some thresholds. Of course, the dual constraint (TIC3) has the shortest average convergence time, which is at least 46.5% shorter in static mode and 5.4% in kinematic mode than standard SF PPP (TIC0).for instance, determines the precise orbit, surface ice flow speed, as well as positioning, navigation and timing (PNT) applications [2][3][4]. The traditional PPP models are based on Ionosphere-Free (IF) combinations with dual-frequency raw phase and code observations for the removal of the first-order effect of ionospheric refraction [5]. However, the second-and third-order ionospheric effects still exist and they may cause measurements errors of sub-centimeters in GPS [6,7]. Therefore, traditional PPP technology based on IF combinations cannot obtain ionospheric information, and the ionospheric error is not completely eliminated. In the last few years, PPP used raw observations has received an increasing amount of research attention because of its several advantages as compared to traditional IF PPP, particularly the development of new multi-frequency GNSS [8].The ionosphere refers to the atmospheric space starting at 60 km above the ground and extending to the magnetosphere. The Global Navigation Satellite System (GNSS) signal will generate ionospheric delay after passing through the ionosphere. A large number of models are currently being used to describe the delay that the ionosphere produces for electromagnetic signals propagating from satellites to receivers. If the delay is not corrected, it can have an important impact on the positioning accuracy of GNS...

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Section: Different Interpolation Methods For Gim Productsmentioning

confidence: 99%

Research on Accelerating Single-Frequency Precise Point Positioning Convergence with Atmospheric Constraint

et al. 2019

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“…Huang and Yuan [3] used time and temporal variation of ionosphere total electron content (TEC) values as a radial-basis function (RBF) network inputs to temporal extrapolation. Jayapal and Zain [4] used a NN with time and solar/geomagnetic indices. Razin and Voosoghi [5] applied a wavelet NN with particle swarm optimization to forecast the TEC over Iran.…”

Section: Introductionmentioning

confidence: 99%

SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area

Kim¹,

Kim²

2021

Remote Sensing

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Space-based augmentation system (SBAS) provides correction information for improving the global navigation satellite system (GNSS) positioning accuracy in real-time, which includes satellite orbit/clock and ionospheric delay corrections. At SBAS service area boundaries, the correction is not fully available to GNSS users and only a partial correction is available, mostly satellite orbit/clock information. By using the geospatial correlation property of the ionosphere delay information, the ionosphere correction coverage can be extended by a spatial extrapolation algorithm. This paper proposes extending SBAS ionosphere correction coverage by using a biharmonic spline extrapolation algorithm. The wide area augmentation system (WAAS) ionosphere map is extended and its ionospheric delay error is compared with the GPS Klobuchar model. The mean ionosphere error reduction at low latitude is 52.3%. The positioning accuracy of the extended ionosphere correction method is compared with the accuracy of the conventional SBAS positioning method when only a partial set of SBAS corrections are available. The mean positioning error reduction is 44.8%, and the positioning accuracy improvement is significant at low latitude.

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“…Leandro and Santos (2006) used geographical information as inputs of a NN model for spatial extrapolation of TEC over Brazil. For spatial extrapolation, Jayapal and Zain (2016) used a NN with time and solar or geomagnetic indices. In addition to these environmental parameters, Kim and Kim (2016) used the ionospheric delay of the inner area to improve the performance of spatial extrapolation.…”

Section: Introductionmentioning

confidence: 99%

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

Kim

2019

Ann. Geophys.

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Abstract. The coverage of regional ionosphere maps is determined by the distribution of ground-based monitoring stations, e.g., GNSS receivers. Since ionospheric delay has a high spatial correlation, ionosphere map coverage can be extended using spatial extrapolation methods. This paper proposes a support vector machine (SVM) to extrapolate the ionosphere map data with solar and geomagnetic parameters. One year of IGS ionospheric delay map data over South Korea is used to train the SVM algorithm. Subsequently, 1 month of ionospheric delay data outside the input data region is estimated. In addition to solar and geomagnetic environmental parameters, the ionospheric delay data from the inner data region are used to estimate the ionospheric delay data for the outside region. The accuracy evaluation is performed at three levels of range −5, 10, and 15∘ outside the inner data regions. The extrapolation errors are 0.33 TECU (total electron content unit) for the 5∘ region and 1.95 TECU for the 15∘ region. These values are substantially lower than the GPS Klobuchar model error values. Comparison with another machine learning extrapolation method, the neural network, shows a substantial improvement of up to 26.7 %.

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Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data

Cited by 5 publications

References 8 publications

Research on Accelerating Single-Frequency Precise Point Positioning Convergence with Atmospheric Constraint

Research on Accelerating Single-Frequency Precise Point Positioning Convergence with Atmospheric Constraint

SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

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