Relevant bibliographies by topics / Ionosphere Estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ionosphere Estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Ionosphere Estimation"

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Elsayed, Ahmed, Ahmed Sedeek, Mohamed Doma, and Mostafa Rabah. "Vertical ionospheric delay estimation for single-receiver operation." Journal of Applied Geodesy 13, no. 2 (April 26, 2019): 81–91. http://dx.doi.org/10.1515/jag-2018-0041.

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Abstract An apparent delay is occurred in GPS signal due to both refraction and diffraction caused by the atmosphere. The second region of the atmosphere is the ionosphere. The ionosphere is significantly related to GPS and the refraction it causes in GPS signal is considered one of the main source of errors which must be eliminated to determine accurate positions. GPS receiver networks have been used for monitoring the ionosphere for a long time. The ionospheric delay is the most predominant of all the error sources. This delay is a function of the total electron content (TEC). Because of the dispersive nature of the ionosphere, one can estimate the ionospheric delay using the dual frequency GPS. In the current research our primary goal is applying Precise Point Positioning (PPP) observation for accurate ionosphere error modeling, by estimating Ionosphere delay using carrier phase observations from dual frequency GPS receiver. The proposed algorithm was written using MATLAB and was named VIDE program. The proposed Algorithm depends on the geometry-free carrier-phase observations after detecting cycle slip to estimates the ionospheric delay using a spherical ionospheric shell model, in which the vertical delays are described by means of a zenith delay at the station position and latitudinal and longitudinal gradients. Geometry-free carrier-phase observations were applied to avoid unwanted effects of pseudorange measurements, such as code multipath. The ionospheric estimation in this algorithm is performed by means of sequential least-squares adjustment. Finally, an adaptable user interface MATLAB software are capable of estimating ionosphere delay, ambiguity term and ionosphere gradient accurately.
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Håkansson, Martin. "Nadir-Dependent GNSS Code Biases and Their Effect on 2D and 3D Ionosphere Modeling." Remote Sensing 12, no. 6 (March 19, 2020): 995. http://dx.doi.org/10.3390/rs12060995.

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Recent publications have shown that group delay variations are present in the code observables of the BeiDou system, as well as to a lesser degree in the code observables of the global positioning system (GPS). These variations could potentially affect precise point positioning, integer ambiguity resolution by the Hatch–Melbourne–Wübbena linear combination, and total electron content estimation for ionosphere modeling from global navigation satellite system (GNSS) observations. The latter is an important characteristic of the ionosphere and a prerequisite in some applications of precise positioning. By analyzing the residuals from total electron content estimation, the existence of group delay variations was confirmed by a method independent of the methods previously used. It also provides knowledge of the effects of group delay variations on ionosphere modeling. These biases were confirmed both for two-dimensional ionosphere modeling by the thin shell model, as well as for three-dimensional ionosphere modeling using tomographic inversion. BeiDou group delay variations were prominent and consistent in the residuals for both the two-dimensional and three-dimensional case of ionosphere modeling, while GPS group delay variations were smaller and could not be confirmed due to the accuracy limitations of the ionospheric models. Group delay variations were, to a larger extent, absorbed by the ionospheric model when three-dimensional ionospheric tomography was performed in comparison with two-dimensional modeling.
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Choi, Bongkwan, Deokhwa Han, Hwigyeom Kim, and Changdon Kee. "A New Method for converting Slant Ionospheric Delays to Vertical for SBAS Ionospheric corrections." E3S Web of Conferences 94 (2019): 03002. http://dx.doi.org/10.1051/e3sconf/20199403002.

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SBAS gives ionospheric correction data to GNSS users for more accurate positioning. SBAS uses obliquity factor of thin shell model to convert slant ionospheric delays to vertical delays which is not precise especially at lower elevation angle. This paper suggests a new method for estimating the vertical delays which are needed for generating the ionospheric corrections. The new method uses Chapman profile assumption by which the vertical electron density distribution can be modeled. It is also assumed that Chapman profile’s parameters are given and the vertical ionospheric delay can be linearly modeled. We divided ionosphere into multi-layer and mapped ionospheric vertical delays with a linear function. Converting vertical delays of each layer to slant delays, we can set a linear equation for the vertical ionospheric delay at IPP. We used IRI model to get information about ionosphere for simulation to verify our new method. Estimation error of vertical ionospheric delay at IPP was decreased about 40% in average when using our new method.
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An, Xiangdong, Xiaolin Meng, Hua Chen, Weiping Jiang, Ruijie Xi, and Qusen Chen. "Modelling Global Ionosphere Based on Multi-Frequency, Multi-Constellation GNSS Observations and IRI Model." Remote Sensing 12, no. 3 (January 31, 2020): 439. http://dx.doi.org/10.3390/rs12030439.

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With the emergence of BeiDou and Galileo as well as the modernization of GPS and GLONASS, more available satellites and signals enhance the capability of Global Navigation Satellite Systems (GNSS) to monitor the ionosphere. However, currently the International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) just use GPS and GLONASS dual-frequency observations in ionosphere estimation. To better determine the global ionosphere, we used multi-frequency, multi-constellation GNSS observations and a priori International Reference Ionosphere (IRI) to model the ionosphere. The newly estimated ionosphere was represented by a spherical harmonic expansion function with degree and order of 15 in a solar-geomagnetic frame. By collecting more than 300 stations with a global distribution, we processed and analysed two years of data. The estimated ionospheric results were compared with those of IAACs, and the averaged Root Mean Squares (RMS) of Total Electron Content (TEC) differences for different solutions did not exceed 3 TEC Unit (TECU). Through validation by satellite altimetry, it was suggested that the newly established ionosphere had a higher precision than the IGS products. Moreover, compared with IGS ionospheric products, the newly established ionosphere showed a more accurate response to the ionosphere disturbances during the geomagnetic storms.
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Gerzen, Tatjana, David Minkwitz, Michael Schmidt, and Eren Erdogan. "Analysis of different propagation models for the estimation of the topside ionosphere and plasmasphere with an ensemble Kalman filter." Annales Geophysicae 38, no. 6 (November 10, 2020): 1171–89. http://dx.doi.org/10.5194/angeo-38-1171-2020.

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Abstract. The accuracy and availability of satellite-based applications, like Global Navigation Satellite System (GNSS) positioning and remote sensing, crucially depend on the knowledge of the ionospheric electron density distribution. The tomography of the ionosphere is one of the major tools for providing links to specific ionospheric corrections and studying and monitoring physical processes in the ionosphere and plasmasphere. In this work, we apply an ensemble Kalman filter (EnKF) approach for the 4D electron density reconstruction of the topside ionosphere and plasmasphere, with the focus on the investigation of different propagation models, and compare them with the iterative reconstruction technique of simultaneous multiplicative column normalized method plus (SMART+). The slant total electron content (STEC) measurements of 11 low earth orbit (LEO) satellites are assimilated into the reconstructions. We conduct a case study on a global grid with altitudes between 430 and 20 200 km, for two periods of the year 2015, covering quiet to perturbed ionospheric conditions. Particularly the performance of the methods for estimating independent STEC and electron density measurements from the three Swarm satellites is analysed. The results indicate that the methods of EnKF, with exponential decay as the propagation model, and SMART+ perform best, providing, in summary, the lowest residuals.
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Ogryzek, Marek, Anna Krypiak-Gregorczyk, and Paweł Wielgosz. "Optimal Geostatistical Methods for Interpolation of the Ionosphere: A Case Study on the St Patrick’s Day Storm of 2015." Sensors 20, no. 10 (May 16, 2020): 2840. http://dx.doi.org/10.3390/s20102840.

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Geostatistical Analyst is a set of advanced tools for analysing spatial data and generating surface models using statistical and deterministic methods available in ESRI ArcMap software. It enables interpolation models to be created on the basis of data measured at chosen points. The software also provides tools that enable analyses of the data variability, setting data limits and checking global trends, as well as creating forecast maps, estimating standard error and probability, making various surface visualisations, and analysing spatial autocorrelation and correlation between multiple data sets. The data can be interpolated using deterministic methods providing surface continuity, and also by stochastic techniques like kriging, based on a statistical model considering data autocorrelation and providing expected interpolation errors. These properties of Geostatistical Analyst make it a valuable tool for modelling and analysing the Earth’s ionosphere. Our research aims to test its applicability for studying the ionosphere, and ionospheric disturbances in particular. As raw source data, we use Global Navigation Satellite Systems (GNSS)-derived ionospheric total electron content. This paper compares ionosphere models (maps) developed using various interpolation methods available in Geostatistical Analyst. The comparison is based on several indicators that can provide the statistical characteristics of an interpolation error. In this contribution, we use our own method, the parametric assessment of the quality of estimation (MPQE). Here, we present analyses and a discussion of the modelling results for various states of the ionosphere: On the disturbed day of the St Patrick’s Day geomagnetic storm of 2015, one quiet day before the storm and one day after its occurrence, reflecting the ionosphere recovery phase. Finally, the optimal interpolation method is selected and presented.
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Khoptar, Alina, and Stepan Savchuk. "Estimation of Ionospheric Delay Influence on the Efficiency of Precise Positioning of Multi-GNSS Observations." Baltic Surveying 12 (June 29, 2020): 14–18. http://dx.doi.org/10.22616/j.balticsurveying.2020.002.

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Currently, Global Navigation Satellite Systems (GNSS) are developing at a fairly rapid pace. Over the last years US GPS and Russian GLONASS were modernizing, whilst new systems like European Galileo and Chinese BDS are launched. The modernizations of the existing and the deployment of new GNSS made a whole range of new signals available to the users, and create a new concept  multi-GNSS. Ionospheric delay is one of the major error sources in multi-GNSS observations. At present, GNSS users usually eliminate the influence of ionospheric delay of the first order items by dual-frequency ionosphere-free combinations. But there is still residual ionospheric delay error of higher orders. In this paper we present four different processing scenarios to exclude the higher orders ionospheric delay effects on multi-GNSS Precise Point Positioning (PPP) performance, including: “only GPS” and “GPS+GLONASS+Galileo+BDS” – without/with eliminating ionospheric delay error of higher orders. Dataset collected from one GNSS station BOR1 (Borowiec, Poland) over almost two years provided by multi-GNSS experiment (MGEX) were used for dual-frequency PPP tests with one- and quadconstellation signals. For the second pair of scenarios were used a IONosphere map EXchange format (IONEX) that supports the exchange of 2- and 3-dimensional TEC maps given in a geographic grid. Numeric experiments show that, the results of different pairs of scenarios differ at the submillimeter level. The results also show that the multi-GNSS processing are better than those based on “only GPS”.
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Ma, G., and T. Maruyama. "Derivation of TEC and estimation of instrumental biases from GEONET in Japan." Annales Geophysicae 21, no. 10 (October 31, 2003): 2083–93. http://dx.doi.org/10.5194/angeo-21-2083-2003.

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Abstract. This paper presents a method to derive the ionospheric total electron content (TEC) and to estimate the biases of GPS satellites and dual frequency receivers using the GPS Earth Observation Network (GEONET) in Japan. Based on the consideration that the TEC is uniform in a small area, the method divides the ionosphere over Japan into 32 meshes. The size of each mesh is 2° by 2° in latitude and longitude, respectively. By assuming that the TEC is identical at any point within a given mesh and the biases do not vary within a day, the method arranges unknown TECs and biases with dual GPS data from about 209 receivers in a day unit into a set of equations. Then the TECs and the biases of satellites and receivers were determined by using the least-squares fitting technique. The performance of the method is examined by applying it to geomagnetically quiet days in various seasons, and then comparing the GPS-derived TEC with ionospheric critical frequencies (foF2). It is found that the biases of GPS satellites and most receivers are very stable. The diurnal and seasonal variation in TEC and foF2 shows a high degree of conformity. The method using a highly dense receiver network like GEONET is not always applicable in other areas. Thus, the paper also proposes a simpler and faster method to estimate a single receiver’s bias by using the satellite biases determined from GEONET. The accuracy of the simple method is examined by comparing the receiver biases determined by the two methods. Larger deviation from GEONET derived bias tends to be found in the receivers at lower (<30° N) latitudes due to the effects of equatorial anomaly.Key words. Ionosphere (mid-latitude ionosphere; instruments and techniques) – Radio science (radio-wave propagation)
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Hargreaves, J. K., and M. Friedrich. "The estimation of D-region electron densities from riometer data." Annales Geophysicae 21, no. 2 (February 28, 2003): 603–13. http://dx.doi.org/10.5194/angeo-21-603-2003.

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Abstract. At high latitude the hard electron precipitation associated with auroral activity is a major source of ionization for the D-region, one consequence being the absorption of radio waves. Direct measurements of the D-region electron density are not readily available, however. This paper investigates the relationship between the electron density at altitudes between 100 and 70 km and the total radio absorption observed with a riometer, with a view to using the latter to predict the former. Tables are given of the median electron density corresponding to 1 dB absorption at 27.6 MHz for each hour of the day, and it is shown that at certain heights the estimates will be accurate to within a factor of 1.6 on 50% of the occasions. A systematic variation with time of day is probably associated with a progressive hardening of the typical electron spectrum during the morning hours. There is also evidence for a seasonal effect possibly due to seasonal variations of the mesosphere.Key words. Ionosphere (auroral ionosphere) – Radio science (ionospheric propagation; instruments and techniques)
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Wang, Jin, Guanwen Huang, Peiyuan Zhou, Yuanxi Yang, Qin Zhang, and Yang Gao. "Advantages of Uncombined Precise Point Positioning with Fixed Ambiguity Resolution for Slant Total Electron Content (STEC) and Differential Code Bias (DCB) Estimation." Remote Sensing 12, no. 2 (January 17, 2020): 304. http://dx.doi.org/10.3390/rs12020304.

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The determination of slant total electron content (STEC) between satellites and receivers is the first step for establishing an ionospheric model. However, the leveling errors, caused by the smoothed ambiguity solutions in the carrier-to-code leveling (CCL) method, degrade the performance of ionosphere modeling and differential code bias (DCB) estimation. To reduce the leveling errors, an uncombined and undifferenced precise point positioning (PPP) method with ambiguity resolution (AR) was used to directly extract the STEC. Firstly, the ionospheric observables were estimated with CCL, PPP float-ambiguity solutions, and PPP fixed-ambiguity solutions, respectively, to analyze the short-term temporal variation of receiver DCB in zero or short baselines. Then, the global ionospheric map (GIM) was modeled using three types of ionospheric observables based on the single-layer model (SLM) assumption. Compared with the CCL method, the slight variations of receiver DCBs can be obviously distinguished using high precise ionospheric observables, with a 58.4% and 71.2% improvement of the standard deviation (STD) for PPP float-ambiguity and fixed-ambiguity solutions, respectively. For ionosphere modeling, the 24.7% and 27.9% improvements for posteriori residuals were achieved for PPP float-ambiguity and fixed-ambiguity solutions, compared to the CCL method. The corresponding improvement for residuals of the vertical total electron contents (VTECs) compared with the Center for Orbit Determination in Europe (CODE) final GIM products in global accuracy was 9.2% and 13.7% for PPP float-ambiguity and fixed-ambiguity solutions, respectively. The results show that the PPP fixed-ambiguity solution is the best one for the GIM product modeling and satellite DCBs estimation.
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Dissertations / Theses on the topic "Ionosphere Estimation"

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Mao, Xiaolei. "GPS CARRIER SIGNAL PARAMETERS ESTIMATION UNDER IONOSPHERE SCINTILLATION." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1314295002.

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Miladinovich, Daniel Sveta. "Data Assimilation for Ionosphere-Thermosphere Storm-Time State Estimation." Thesis, Illinois Institute of Technology, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10843813.

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This dissertation presents a data assimilation method for estimating the physical drivers of the Earth's ionosphere layer through the combination of Global Navigation Satellite System based (GNSS) ionospheric density measurements, Fabry-Perot interferometer (FPI) neutral wind measurements and several empirical models. The main contributions include: 1) Kalman filtering for multi-observation ingestion and multi-state estimation, 2) ingestion of FPI neutral wind measurements, 3) spherical harmonic basis functions for global electric potential estimation and 4) a study of storm-time ion drifts using globally ingested data.

The thermosphere is a region of Earth's atmosphere (80-1000 km) that contains a balance of particle density and solar ionizing radiation such that an ionosphere can form. During geomagnetic storm events, the ionosphere can be disturbed causing abrupt redistribution of the ionospheric plasma. These disruptions can cause blackouts for radio wave-based communications and navigation systems. Understanding what causes the ionosphere to change is therefore necessary as society becomes more dependent on navigation and communication technologies.

The first step in understanding the ionosphere is to quantify its physical drivers. Measurements of the ionosphere are limited both spatially and temporally because the region is so vast. Models, on the other hand, provide our best understanding and capability to simulate the ionosphere and its drivers but often fall short in capturing certain phenomena during severe geomagnetic storms. In this work, a data assimilation algorithm called Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) is further developed to combine both measurements and simulation data sets for estimating ionospheric drivers globally. EMPIRE ingests ionosphere plasma density rate measurements and subtracts model simulation results to produce an observation of the difference between measurements and simulation. EMPIRE then fits basis functions which represent physical drivers to the measurement-simulation discrepancy. The mapping from observation to physical driver happens using the ion continuity governing equation as a model.

The EMPIRE algorithm was originally developed in 2009 to perform regional data assimilation and used only plasma density measurements. In this work, EMPIRE is modified to use a Kalman filter so measurements and models can be ingested in an efficient and systematic manner. Direct physical driver measurements are provided by FPI neutral wind measurements using the newly developed Kalman filter. This thesis demonstrates the first ever use of FPIs and plasma density measurements in a data assimilative environment. Next, EMPIRE is modified to estimate coefficients to spherical harmonic basis functions rather than power series basis functions. Spherical harmonic functions allow EMPIRE to provide global estimates because they are continuous and orthogonal on a spherical domain (such as Earth). A study is then conducted to ingest global plasma density rate measurements and neutral winds to estimate ion drifts across the globe.

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Sauerwein, Kevin Lee. "Nonlinear State Estimation of the Ionosphere as Perturbed by the 2017 Great American Eclipse." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87581.

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The 2017 Great American Eclipse provided an excellent opportunity for scientists and engineers to study the ionosphere. The dynamics of the ionosphere are affected by the amount of solar radiation it receives and a total solar eclipse produces a short perturbation to the incoming solar radiation. Analyzing how the ionosphere reacts to this type perturbation could lead to new levels of understanding of it. This study develops a nonlinear filter that estimates the state of the ionosphere's 3-D electron density profile given total electron content (TEC) measurements from dual-frequency GPS receivers located on the ground and on low-Earth-orbiting spacecraft. The electron density profile is parameterized by a bi-quintic latitude/longitude spline of Chapman Profile parameters that define the vertical electron density profile. These Chapman parameters and various latitude and longitude partial derivatives are defined at a set of latitude/longitude spline grid points. Bi-quintic interpolation between the points defines the parameters' values and the corresponding Chapman profiles at all latitude/longitude points. The Chapman parameter values and their partial derivatives at the latitude/longitude spline nodes constitute the unknowns that the nonlinear filter estimates. The filter is tested with non-eclipse datasets to determine its reliability. It performs well but does not estimate the biases of the receivers as precisely as desired. Many attempts to improve the filter's bias estimation ability are presented and tried. Eclipse datasets are input to the filter and analyzed. The filter produced results that suggest that the altitude of peak electron density increased significantly near and within the eclipse path and that the vertical TEC (VTEC) was drastically decreased near and within the eclipse path. The changes in VTEC and altitudes of peak electron density caused by the eclipse leave a lasting effect that alters the density profile for anywhere from 15 minutes to several hours.
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The 2017 Great American Eclipse garnered much attention in the media and scientific community. Solar eclipses provide unique opportunities to observe the ionosphere’s behavior as a result of irregular solar radiation patterns. Many devices are used to measure this behavior, including GPS receivers. Typically, GPS receivers are used to navigate by extracting and combining carrier phase and pseudorange data from signals of at least four GPS satellites. When the position of a GPS receiver is well-known, information about the portion of the ionosphere that the signal traveled through can be estimated from the GPS signals. This estimation procedure has been done with ground-based and orbiting GPS receivers. However, fusing the two data sources has never been done and will be a primary focus of this study. After demonstrating the performance of the estimation algorithm, it is used to estimate the state of the ionosphere as it was perturbed by the 2017 Great American Eclipse.
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Brown, Neil E. "Sequential phased estimation of ionospheric path delays for improved ambiguity resolution over long GPS baselines /." Connect to thesis, 2006. http://eprints.unimelb.edu.au/archive/00003170.

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Foster, Matthew. "Reconstruction and motion estimation of sparsely sampled ionospheric data." Thesis, University of Bath, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503658.

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This thesis covers two main areas which are related to the mapping and examination of the ionosphere. The first examines the performance and specific nuances of various state-of-the-art interpolation methods with specific application to mapping the ionosphere. This work forms the most widely scoped examination of interpolation technique for ionospheric imaging to date, and includes the introduction of normalised convolution techniques to geophysical data. In this study, adaptive-normalised convolution was found to perform well in ionospheric electron content mapping, and the popular technique, kriging was found to have problems which limit its usefulness. The second, is the development and examination of automatic data-driven motion estimation methods for use on ionospheric electron content data. Particular emphasis is given to storm events, during which characteristic shapes appear and move across the North Pole. This is a particular challenge, as images covering this region tend to have a very-low resolution. Several motion estimation methods are developed and applied to such data, including methods based on optical flow, correlation and boundarycorrespondence. Correlation and relaxation labelling based methods are found to perform reasonably, and boundary based methods based on shape-context matching are found to perform well, when coupled with a regularisation stage. Overall, the techniques examined and developed here will help advance the process of examining the features and morphology of the ionosphere, both during storms and quiet times.
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ROCHA, Gilmara Dannielle de Carvalho. "Avaliação e mitigação dos efeitos ionosféricos no posicionamento por ponto preciso GNSS no Brasil." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16056.

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Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-03-17T18:13:34Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) GILMARA DANNIELLE DE CARVALHO ROCHA_ DISSERTAÇÃO 2015.pdf: 3108174 bytes, checksum: c5307dded72886ffaf2f476a6333026d (MD5)
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CNPq
Umas das maiores fontes causadoras de erro no posicionamento GNSS é a ionosfera, sendo que o efeito provocado por esta camada da atmosfera é um dos mais impactantes no processo de estimativa das coordenadas, principalmente para dados coletados com receptores de simples frequência. A modelagem matemática da refração ionosférica é complexa devido às variações diárias, sazonais, de curto e longo período, além de outros fenômenos que ocorrem na atmosfera, tal como a cintilação ionosférica. Em se tratando de posicionamento absoluto com receptores de simples frequência, seja Posicionamento por Ponto Simples (PP) ou Posicionamento por Ponto Preciso (PPP), estratégia adequada de correção dos efeitos ionosféricos devem ser adotadas. A correção da ionosfera para dados de simples frequência pode ser realizada a partir de modelo matemático, tal como o de Klobuchar, Mapas Globais ou Regionais da Ionosfera ou a partir da estimativa residual da ionosfera. Quando se tem disponível dados de duas frequências é possível utilizar a combinação ion-free, a qual permite eliminar os efeitos de primeira ordem da ionosfera. Contudo esta combinação faz com que as ambiguidades percam suas características de números inteiros, bem como realça outros níveis de ruído tal como o multicaminho. Uma possibilidade para atenuar os efeitos da ionosfera é a aplicação da estimativa dos efeitos residuais junto com as coordenadas incógnitas da estação e outros parâmetros. Neste caso, os efeitos da ionosfera podem ser tratados como um processo estocástico no Filtro de Kalman e se pode aplicar tal estratégia para dados de simples ou dupla frequência. Essa estratégia pode facilitar a solução das ambiguidades como inteiras e consequentemente permite a obtenção de resultados mais acurados no posicionamento geodésico. Dentro deste contexto, esta dissertação de mestrado apresenta a avaliação da acurácia do posicionamento absoluto GPS com aplicação de diferentes estratégias de correção da ionosfera. Foram realizados processamentos no modo PPP com dados GPS coletados em estações da RBMC em períodos de alta e baixa atividade solar para os anos de 2010 a 2013, onde se aplicou a correção da ionosfera advinda do modelo de Klobuchar, dos mapas globais (GIM – Global Ionospheric Map) e regionais (LPIM – La Plata Ionospheric Model), além da estimativa residual da ionosfera. As coordenadas estimadas foram comparadas com aquelas advindas da solução semanal SIRGAS-CON, a qual é dada atualmente em ITRF2008 e o Erro Médio Quadrático (EMQ), seja diário ou anual foi utilizado como medidor de acurácia. Ao aplicar as correções da ionosfera advinda dos mapas globais e regionais na estimativa de coordenadas no PPP utilizando somente medidas de código, observou-se melhoria de até 80% em relação ao PPP sem correção da ionosfera. O PPP com correção ionosférica advinda dos mapas regionais produziu melhorias diárias da ordem de 10% em relação ao uso dos mapas globais. Com base nas melhorias produzidas com a utilização do modelo ionosférico regional, foi proposta a modificação do modelo estocástico do ajustamento tendo em vista que somente o modelo funcional é afetado pelas correções ionosféricas advindas dos mapas. Com relação à estimativa residual da ionosfera foram realizados experimentos envolvendo medidas de código e fase na frequência L1 com geração de séries temporais anuais de coordenadas para diversas estações da RBMC, cuja acurácia alcançada foi da ordem de 10 cm no PPP com solução diária.
One of the largest sources of errors in the GNSS positioning is the ionosphere considering that the effect caused by that atmosphere layer is one of the most impacting in the coordinate estimation process, especially for data collected with single frequency receivers. Mathematical modeling of ionospheric refraction is complex due to daily variation in as well as, seasonal short and long period and also other phenomena occurring in the atmosphere such as ionospheric scintillation. Concerning the absolute positioning with single frequency receivers, whether Single Point Positioning (PP) or by Precise Point Positioning (PPP), appropriate strategy to correct the ionospheric effects should be adopted. The ionosphere correction for single frequency data can be performed from mathematical model, such as Klobuchar, Global or Regional Ionosphere maps or from residual ionosphere estimating. When one has available data from two frequencies it is possible to apply the ionosphere free combination which allows eliminating the first order ionosphere effects. However, this combination makes ambiguities lose its integer characteristics as well as amplify other noise levels as for instance multipath. One possibility to mitigate the ionosphere effects is the application of the ionosphere residual estimation along with coordinates station and other parameters. In this case, the ionosphere effects can be treated as a stochastic process in the Kalman filter where it is possible to apply that strategy for single or dual frequency data. This strategy can facilitate the integer ambiguities resolutions and consequently allows obtaining more accurate results in geodetic positioning. Inside this context, this master thesis presents the accuracy evaluation of the GPS absolute positioning by applying different strategies for ionosphere corrections. Processing was performed in PPP mode with GPS data collected in brazilizan RBMC stations in periods of high and low solar activities for the years 2010-2013, where it was applied ionosphere correction from Klobuchar model, global (GIM - Global Ionospheric Map) and regional (LPIM - La Plata Ionospheric Model) maps and the residual ionosphere estimation. The estimated coordinates were compared with those coming from SIRGAS-CON in a weekly solution which is currently given in ITRF2008 and Root Mean Square (RMS), either daily or annually, was used as accuracy measuring. When applying ionosphere corrections from global and regional maps in the PPP coordinates estimation using only code measurements, it was observed improvements of up to 80% comparing with PPP without ionosphere correction. The PPP with ionospheric correction coming from regional maps produced daily improvements of around 10% in relation to applying global maps. Based on improvements reached with corrections from regional ionospheric model, it was proposed the modification of the stochastic model for adjustment considering that only the functional model is affected by the ionospheric corrections coming from maps. Regarding the residual ionosphere estimation experiments were performed involving code and phase measurements in the L1 frequency with generation of coordinates annual time series considering the chosen RBMC stations whose accuracy achieve approximately 10 cm in PPP with daily solution.
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Johnson, Andrea Marie S. M. Massachusetts Institute of Technology. "Optimal estimation of ionosphere-induced group delays of global positioning satellite signals during launch, orbit and re-entry." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/62968.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 237-238).
There are many sources of range error in a Global Positioning Satellite (GPS) signal that has traveled to a receiver near the earth's surface. Among these is the ionospheric group delay. In the past, a single-state, dual-frequency filter has been used to estimate the ionospheric delay for authorized users. Although sufficient for terrestrial receivers for which the ionospheric delay changes very slowly, such a filter is inadequate for space-based missions in which a receiver passes rapidly through the ionosphere. Various Kalman filters are examined and simulation results presented. The most robust Kalman filter considered was a seven-state filter. This filter utilizes four measurements: dual-frequency pseudo-range differencing, dual-frequency delta-range differencing, and single-frequency rate measurements for both frequencies (LI and L2). Two states are necessary for the model dynamics plus five constant states necessary for processing rate measurements. The process model selected for the seven-state filter was the integral of a first-order Markov process. The filter was used to estimate both the ionospheric group delay and the deviation of the delay from a given reference model. When used to estimate the deviation of the delay from a reference model, the group delay transitioned from "estimated" to "modeled" smoothly in the absence of measurements. In the absence of measurements, the estimated group delay tends to a bias from the reference model provided.
by Andrea Marie Johnson.
S.M.
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Debchoudhury, Shantanab. "Parameter Estimation from Retarding Potential Analyzers in the Presence of Realistic Noise." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88466.

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Retarding Potential Analyzers (RPA) have a rich flight heritage. These instruments are largely popular since a single current-voltage (I-V) profile can provide in-situ measurements of ion temperature, velocity and composition. The estimation of parameters from an RPA I-V curve is affected by grid geometries and non-ideal biasing which have been studied in the past. In this dissertation, we explore the uncertainties associated with estimated ion parameters from an RPA in the presence of instrument noise. Simulated noisy I-V curves representative of those expected from a mid-inclination low Earth orbit are fitted with standard curve fitting techniques to reveal the degree of uncertainty and inter-dependence between expected errors, with varying levels of additive noise. The main motive is to provide experimenters working with RPA data with a measure of error scalable for different geometries. In subsequent work, we develop a statistics based bootstrap technique designed to mitigate the large inter-dependency between spacecraft potential and ion velocity errors, which were seen to be highly correlated when estimated using a standard algorithm. The new algorithm - BATFORD, acronym for "Bootstrap-based Algorithm with Two-stage Fit for Orbital RPA Data analysis" - was applied to a simulated dataset treated with noise from a laboratory calibration based realistic noise model, and also tested on real in-flight data from the C/NOFS mission. BATFORD outperforms a traditional algorithm in simulation and also provides realistic in-situ estimates from a section of a C/NOFS orbit when the satellite passed through a plasma bubble. The low signal-to-noise ratios (SNR) of measured I-Vs in these bubbles make autonomous parameter estimation notoriously difficult. We thus propose a method for robust autonomous analysis of RPA data that is reliable in low SNR environments, and is applicable for all RPA designs.
Doctor of Philosophy
The plasma environment in Earth’s upper atmosphere is dynamic and diverse. Of particular interest is the ionosphere - a region of dense ionized gases that directly affects the variability in weather in space and the communication of radio wave signals across Earth. Retarding potential analyzers (RPA) are instruments that can directly measure the characteristics of this environment in flight. With the growing popularity of small satellites, these probes need to be studied in greater detail to exploit their ability to understand how ions - the positively charged particles- behave in this region. In this dissertation, we aim to understand how the RPA measurements, obtained as current-voltage relationships, are affected by electronic noise. We propose a methodology to understand the associated uncertainties in the estimated parameters through a simulation study. The results show that a statistics based algorithm can help to interpret RPA data in the presence of noise, and can make autonomous, robust and more accurate measurements compared to a traditional non-linear curve-fitting routine. The dissertation presents the challenges in analyzing RPA data that is affected by noise and proposes a new method to better interpret measurements in the ionosphere that can enable further scientific progress in the space physics community.
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Alshammari, Roghailanm. "Ionospheric estimation using tomography and GPS Ll and L2 phase observables." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489294.

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The ionosphere, extending from 50 km to 1,000 km above the Earth's surface, is a region of free electrons that cause errors in the measurements of Global Positioning System (GPS) signals. For GPS static studies the ionospheric delay can either be modelled or recovered from dual frequency receivers. However, for real-time kinematic GPS the ionosphere is a major obstacle to carrier phase ambiguity resolution over long baselines with possible aliasing between vertical motion and the ionospheric delay. Several researchers have demonstrated that regional tomographic models of the ionosphere can be constructed from a network of static GPS receivers for implementation in kinematic processing. The purpose of this research is to create mathematically a layered ionospheric tomographic model and analyse a three dimensional (3D) one layer ionospheric tomographic model, voxel-based, for the ionosphere based on GPS data observed from dual-frequency GPS tracking stations, using the carrier phase as the principal observable. This model is fully integrated within Kalman filtering that combines all the parameters and updates the corrections at each epoch. The tomographic model observation equations utilise the two carrier phases (L1and L2 ) directly to estimate the ionospheric parameters. The model thus differs from others that typically use a linear combination of L1and L2 • The model will be used for GPS kinematic processing for long baselines to provide an accurate estimation of the electron density with the purpose of obtaining optimal accuracy of the slant delays due to the ionosphere. A double difference method has been implemented in order to perform the ionospheric measurements and to eliminate possible satellite and receiver dependent biases. The Massachusetts Institute of Technology (MIT) kinematic program, TRACK, has been modified to implement the proposed ionospheric tomographic model. The tomographic ionospheric model has been validated using several days of data from North American GPS stations over an area from about 3t'N to 36'N in Latitude and from 242'E to 246'E in Longitude, forming baselines ranging from 230 km to 400 km. The performance of the estimated ionosphere model has been validated against the United States-Total Electron Content (US-TEC) model. RMS comparisons ranged from 1 TECU (Total Electron Content Unit) to 3 TECU. A comparison of the derived ionosphere maps by using the current US-TEC with the corresponding results extracted using the US-TEC of the preceding day, shows a maximum of 1 TECU difference between maps at hourly intervals, a maximum rms differences of less than 0.5 TECU and a maximum formal uncertainty of less than 1.2 TECU over the area of study. The tests also indicated that the ionospheric tomographic model is robust with enhanced capability comparable to the ionospheric free linear combination (Lc) solutions in terms of GPS phase residuals and ambiguity resolution. On utilising the ionospheric tomographic model the phase residuals rms are typically 4 mm compared with 11 mm on using Lc, while the ambiguity resolution increases to about 84% compared with 78% with Lc. In addition, the average difference between the tropospheric results of the ionospheric tomographic model and Lc is about 10 mm.
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Kim, Y. S., and R. Eng. "Estimation of Tec and Range of EMP Source Using an Improved Ionospheric Correction Model." International Foundation for Telemetering, 1992. http://hdl.handle.net/10150/611957.

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International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California
An improved ionospheric delay correction model for a transionospheric electromagnetic pulse (EMP) is used for estimating the total-electron-content (TEC) profile of the path and accurate ranging of the EMP source. For a known pair of time of arrival (TOA) measurements at two frequency channels, the ionospheric TEC information is estimated using a simple numerical technique. This TEC information is then used for computing ionospheric group delay and pulse broadening effect correction to determine the free space range. The model prediction is compared with the experimental test results. The study results show that the model predictions are in good agreement with the test results.
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Books on the topic "Ionosphere Estimation"

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Lin, Lao-Sheng. Real-time estimation of ionospheric delay using GPS measurements. Sydney, NSW: School of Geomatic Engineering, University of New South Wales, 1998.

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S, Jacobs C., and Jet Propulsion Laboratory (U.S.), eds. Observation model and parameter partials for the JPL VLBI parameter estimation software "MODEST"--1994. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1994.

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Book chapters on the topic "Ionosphere Estimation"

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Wang, Denghui, Chengfa Gao, and Shuguo Pan. "Single-Epoch Integer Ambiguity Resolution for Long-Baseline RTK with Ionosphere and Troposphere Estimation." In Lecture Notes in Electrical Engineering, 125–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37398-5_12.

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Bidikar, Bharati, G. Sasibhushana Rao, and Ganesh Laveti. "Ionospheric Time Delay Estimation Algorithm for GPS Applications." In Advances in Intelligent Systems and Computing, 259–67. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5272-9_25.

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Yang, Rong, Xingqun Zhan, and Jihong Huang. "Robust GNSS Triple-Carrier Joint Estimations Under Strong Ionosphere Scintillation." In Lecture Notes in Electrical Engineering, 562–75. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3707-3_53.

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Alken, Patrick. "Estimating Currents and Electric Fields at Low Latitudes from Satellite Magnetic Measurements." In Ionospheric Multi-Spacecraft Analysis Tools, 233–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26732-2_11.

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Wang, Cheng, Jiexian Wang, and Yu Morton. "Regional Ionospheric TEC Gradients Estimation Using a Single GNSS Receiver." In Lecture Notes in Electrical Engineering, 363–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54743-0_30.

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Liu, Dun, Xiao Yu, Liang Chen, and Jian Feng. "Irregularities Detection and Bounding Variance Estimation in Ionospheric Grid Model." In China Satellite Navigation Conference (CSNC) 2016 Proceedings: Volume II, 141–49. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0937-2_12.

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Xuguang, Yang, Yu Changjun, Liu Aijun, and Wang Linwei. "Estimating of RCS of Ionosphere for High Frequency Surface Wave Radar." In Machine Learning and Intelligent Communications, 233–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73447-7_27.

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Wang, Huarun, Hongzhou Chai, Min Wang, Zongpeng Pan, and Yang Chong. "Study in BDS Uncombined PPP Ionospheric Delay Estimation and Differential Code Biases." In Lecture Notes in Electrical Engineering, 161–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46632-2_14.

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Lingwal, Yogesh, Fateyh Bahadur Singh, and B. N. Ramakrishna. "Estimation of Differential Code Bias and Local Ionospheric Mapping Using GPS Observations." In Advances in Intelligent Systems and Computing, 809–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_69.

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Zhao, Wei, Min Li, Zhixue Zhang, Jinxian Zhao, Caibo Hu, Na Zhao, and Hui Ren. "Application of Real-Time Multipath Estimation on the GEO Satellite Dual-Frequency Ionospheric Delay Monitoring." In Lecture Notes in Electrical Engineering, 377–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54737-9_33.

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Conference papers on the topic "Ionosphere Estimation"

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Tuna, Hakan, Orhan Ankan, and Feza Ankan. "3D electron density estimation in the ionosphere." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830282.

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Alves, T., and P. Lalande. "Pulse dispersion into the earth-ionosphere waveguide: Lightning distance estimation." In 2015 1st URSI Atlantic Radio Science Conference (URSI AT-RASC). IEEE, 2015. http://dx.doi.org/10.1109/ursi-at-rasc.2015.7303072.

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Kunitsyn, V. E. "Ionospheric effects of solar flares on global navigation satellite systems end estimation of parameters of disturbed ionosphere." In IET 11th International Conference on Ionospheric Radio Systems and Techniques (IRST 2009). IEE, 2009. http://dx.doi.org/10.1049/cp.2009.0073.

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Bagdasaryan, S. T., and Y. D. Shirman. "Estimation of Time Delay and Ionosphere Parameters for Wideband Signal Reception." In 2006 3rd International Conference on Ultrawideband and Ultrashort Impulse Signals. IEEE, 2006. http://dx.doi.org/10.1109/uwbus.2006.307162.

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Zhang, Kexin, Jian Jiao, and Qiming Zeng. "Ionosphere Estimation of the Split-Spectrum InSAR based on IRI Model." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324612.

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Wang, Jun, Yu (Jade) Morton, and Robert Robinson. "Spaced Multi-GNSS Receiver Array as Ionosphere Radar for Irregularity Drift Velocity Estimation during High Latitude Ionospheric Scintillation." In 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.15345.

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Miyake, Taketoshi, Takahiro Kurokawa, Toshimi Okada, and Keigo Ishisaka. "Estimation of spatial structure of lower ionosphere with two-dimensional FDTD simulations." In 2011 XXXth URSI General Assembly and Scientific Symposium. IEEE, 2011. http://dx.doi.org/10.1109/ursigass.2011.6051158.

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Maheshwari, Megha, and Nirmala S. "Estimation of Galileo like ionosphere coefficients using IRNSS data for equatorial region." In 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC). IEEE, 2019. http://dx.doi.org/10.23919/ursiap-rasc.2019.8738186.

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Samanes, Jorge, Jean-Pierre Raulin, and Cao Jinbin. "Estimation of the nighttime height of the lower ionosphere using VLF waves propagation." In 2016 URSI Asia-Pacific Radio Science Conference (URSI AP-RASC). IEEE, 2016. http://dx.doi.org/10.1109/ursiap-rasc.2016.7601185.

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Yue, Wenjue, Bo Peng, Xizhang Wei, and Xiang Li. "Ionosphere effect estimation in micro-Doppler signature extraction for P-band radar targets." In 2017 Progress In Electromagnetics Research Symposium - Spring (PIERS). IEEE, 2017. http://dx.doi.org/10.1109/piers.2017.8262052.

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Reports on the topic "Ionosphere Estimation"

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Baker, Zachary Kent. Constrained Shortest Path Estimation on the D-Wave 2X: Accelerating Ionospheric Parameter Estimation Through Quantum Annealing. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1331299.

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