A New Technique Based on Grey Model for Forecasting of Ionospheric GPS Signal Delay Using Gagan Data

Venkateswarlu, Ginkala; Sarma, A. D.

doi:10.2528/pierm17041403

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Further, it is noticed that the intensity of scintillations and ROTI is less with increasing latitudes except for Shimla station due to the high values of K p (3 ≤ K p ≤ 6) index. The intensity of scintillations is more for Trivandrum station, as it is closer to geomagnetic equator which further confirms earlier reported findings [22,23].…”

Section: Shimla (3108 • N 7706 • E)supporting

confidence: 90%

Analysis of Correlation Between Roti and S4 Using Gagan Data

Manga¹,

Lakshmanna²,

Sarma³

et al. 2021

PIER M

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As ionosphere is one of the most prominent sources of error, ionospheric TEC and scintillation studies are necessary for improving the performance of a navigation system. In this paper, the behavior of the correlation coefficient (ρ) between Rate of TEC Index (ROTI) and amplitude scintillation index (S 4 ) over low latitude station Hyderabad (Latitude: 17.44 • (deg.) N, Longitude: 78.74 • (deg.) E) for different seasons is analyzed. Also, the analysis is extended for nearly same longitude stations like Trivandrum, Bangalore, Bhopal, Delhi, and Shimla for the higher values of total K p index for 60 days (most disturbed 5 days per month). For Trivandrum (lowest latitude station), it is observed that both S 4 and ROTI are high as compared to Bangalore, Bhopal, Delhi, and Shimla. It is found that there is a good correlation between the temporal variations of ROTI with S 4 after post sunset hours. The confidence intervals for computed correlation coefficients at 95% confidence level are also given.

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Section: Shimla (3108 • N 7706 • E)supporting

confidence: 90%

Analysis of Correlation Between Roti and S4 Using Gagan Data

Manga¹,

Lakshmanna²,

Sarma³

et al. 2021

PIER M

Self Cite

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“…In addition, methods based on neural networks have also been developed [9][10][11][12], along with autocorrelation and autocovariance procedures [13][14][15], linear regression [16,17], and the Grey model by Venkateswarlu and Sarma [18]. On the other hand, methods have been developed that depend on physical models, such as the one considered by the JPL Global Ionosphere Thermosphere Model (GITM) [19].…”

Section: Introductionmentioning

confidence: 99%

TEC Forecasting Based on Manifold Trajectories

et al. 2018

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In this paper, we present a method for forecasting the ionospheric Total Electron Content (TEC) distribution from the International GNSS Service's Global Ionospheric Maps. The forecasting system gives an estimation of the value of the TEC distribution based on linear combination of previous TEC maps (i.e., a set of 2D arrays indexed by time), and the computation of a tangent subspace in a manifold associated to each map. The use of the tangent space to each map is justified because it allows modeling the possible distortions from one observation to the next as a trajectory on the tangent manifold of the map. The coefficients of the linear combination of the last observations along with the tangent space are estimated at each time stamp to minimize the mean square forecasting error with a regularization term. The estimation is made at each time stamp to adapt the forecast to short-term variations in solar activity.

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“…In order to integrate the advantages of AI-based methods and non-AI methods, a novel hybrid algorithm utilising grey system theory and artificial neural network is proposed. Grey system theory is an interdisciplinary scientific area that was introduced in 1980s by Deng (Deng, 1982; 1989) and has been used to compensate for GNSS location fault data (Zhou and Zhang, 2010; Wang et al, 2015; Venkateswarlu and Sarma, 2017). However, the prediction accuracy is often low (Kayacan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent Algorithm DGP-MLP for GNSS/INS Integration during GNSS Outages

Zhang

Wang

2018

J. Navigation

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The performance of Global Navigation Satellite System (GNSS) and Micro-Electro-Mechanical System (MEMS)-based Inertial Navigation System (INS) integrated navigation is reduced during GNSS outages. To bridge the period during GNSS outages, a novel hybrid intelligent algorithm incorporating a Discrete Grey Predictor (DGP) and a Multilayer Perceptron (MLP) neural network (DGP-MLP) is proposed. The DGP-MLP is used to provide a pseudo-GNSS position to correct the INS errors during GNSS outages; the DGP uses the GNSS position information of the latest few moments to predict the position of future moments; in the process of DGP-MLP, the MLP is used to modify the prediction errors of DGP, and the MLP is improved by adding momentum terms and adaptively adjusting the learning rate and momentum factor. To evaluate the effectiveness of the proposed methodology, four GNSS outages in different cases over a real field test data were employed. The experimental results demonstrate that the proposed methodology can significantly improve positioning accuracy during GNSS outages.

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A New Technique Based on Grey Model for Forecasting of Ionospheric GPS Signal Delay Using Gagan Data

Cited by 5 publications

References 14 publications

Analysis of Correlation Between Roti and S4 Using Gagan Data

Analysis of Correlation Between Roti and S4 Using Gagan Data

TEC Forecasting Based on Manifold Trajectories

A Hybrid Intelligent Algorithm DGP-MLP for GNSS/INS Integration during GNSS Outages

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