Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region

Suraj, Puram Sai; Dabbakuti, J. R. K. Kumar; Chowdhary, V. Rajesh; Tripathi, Nimisha

doi:10.1007/s00190-017-1099-6

Cited by 21 publications

(2 citation statements)

References 31 publications

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“…The ordinary kriging is one of the purposive method that is used to predict the unknown value on the observed TEC data [15,16]. Recently, Deep Learning methods has been intensively used to predict the temporal TEC [17,18]. In [19], Support Vector Machine (SVM), which is the one of the Machine Learning technique, has been applied on GPS-TEC data for the detection of earthquake precursors.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Performance of the Regression Models in GPS-Total Electron Content Prediction

2023

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The ionosphere is an important layer that provides radio communication in the upper atmosphere. The ionosphere is located between 50 km and 1000 km above the atmosphere. Electron density, which is the most important parameter of the ionosphere, changes depending on location, time, seasons, altitude, solar, geomagnetic and seismic activity. A significant measurable amount of electron density is Total Electron Content (TEC), which is used to probe the structure of the ionosphere and upper atmosphere. The Global Positioning System (GPS), which has a low cost and widespread receiver network is prominent used in TEC estimation. The IONOLAB-TEC data estimated from GPS is used in this study. Prediction of TEC is important phenomenon to operate and to plan the Earth-space and satellite-to-satellite communication systems, to generate the earthquake precursor signals using TEC and to detect of anomalies in the ionosphere. In this study, IONOLAB-TEC data obtained from GPS is estimated using regression models. Among the tested algorithms, it is observed that the Exponential Gaussian Process Regression and Interactions Linear Regression algorithms are very successful and high-performance models for TEC estimation.

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Section: Introductionmentioning

confidence: 99%

Comparison of the Performance of the Regression Models in GPS-Total Electron Content Prediction

2023

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“…Signal processing and its various advancements like stochastic signal processing (Allen and Blackman, 1991; Lindgren and Gong, 1978), adaptive signal processing and multirate signal processing have wide range of applications GPS-TEC variations (Suraj et al , 2018; Ratnam et al , 2019), medical field (Bhagat et al , 2018; Putluri et al , 2019; Nagaraju et al , 2019), wireless communications (Mahender et al ., 2017) and computer vision models (Prasad et al , 2017; Kishore and Prasad, 2017; Maddala et al , 2019). In stochastic signal processing, underwater target tracking has a lot of applications like underwater surveillance, finding ship decks, school of fish, target tracking, etc.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of shifted Rayleigh filter for passive surveillance in underwater

Divya

Rao

2021

IJICC

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PurposeFrom many decades, bearings-only tracking (BOT) is the interested problem for researchers. This utilises nonlinear filtering methods for state estimation as there is only information about the target, i.e. bearing is a nonlinear measurement. The measurement bearing is tangentially related to the target state vector. There are many nonlinear filtering algorithms developed so far in the literature.Design/methodology/approachIn this research work, the recently developed nonlinear filtering algorithm, i.e. shifted Rayleigh filter (SRF), is applied to BOT.FindingsThe SRF is tested for two-dimensional BOT against various scenarios. The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm, unscented Kalman filter (UKF).Originality/valueSRF utilises the nonlinearities present in the bearing measurement through the use of moment matching. The SRF is able to produce the solution in highly noisy environment, long ranges and high dimension tracking.

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Retrieve, Processing and Analysis of Global Positioning System Derived Ionospheric Total Electron Content Using IGS Products

Dabbakuti

Chandu

Reddy

et al. 2021

Lecture Notes in Electrical Engineering

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Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region

Cited by 21 publications

References 31 publications

Comparison of the Performance of the Regression Models in GPS-Total Electron Content Prediction

Comparison of the Performance of the Regression Models in GPS-Total Electron Content Prediction

Prevalence of shifted Rayleigh filter for passive surveillance in underwater

Retrieve, Processing and Analysis of Global Positioning System Derived Ionospheric Total Electron Content Using IGS Products

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