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Статті в журналах з теми "Ionosphere Estimation"
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.
Повний текст джерелаHåkansson, 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.
Повний текст джерелаChoi, 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.
Повний текст джерелаAn, 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.
Повний текст джерелаGerzen, 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.
Повний текст джерелаOgryzek, 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.
Повний текст джерелаKhoptar, 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.
Повний текст джерелаMa, 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.
Повний текст джерелаHargreaves, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаДисертації з теми "Ionosphere Estimation"
Mao, Xiaolei. "GPS CARRIER SIGNAL PARAMETERS ESTIMATION UNDER IONOSPHERE SCINTILLATION." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1314295002.
Повний текст джерелаMiladinovich, Daniel Sveta. "Data Assimilation for Ionosphere-Thermosphere Storm-Time State Estimation." Thesis, Illinois Institute of Technology, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10843813.
Повний текст джерелаThis 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.
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.
Повний текст джерелаMS
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.
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.
Повний текст джерелаFoster, 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.
Повний текст джерелаROCHA, 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.
Повний текст джерелаMade available in DSpace on 2016-03-17T18:13:34Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) GILMARA DANNIELLE DE CARVALHO ROCHA_ DISSERTAÇÃO 2015.pdf: 3108174 bytes, checksum: c5307dded72886ffaf2f476a6333026d (MD5) Previous issue date: 2015-03-06
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.
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.
Повний текст джерелаCataloged 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.
Debchoudhury, Shantanab. "Parameter Estimation from Retarding Potential Analyzers in the Presence of Realistic Noise." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88466.
Повний текст джерелаDoctor 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.
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.
Повний текст джерелаKim, 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.
Повний текст джерелаAn 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.
Книги з теми "Ionosphere Estimation"
Lin, Lao-Sheng. Real-time estimation of ionospheric delay using GPS measurements. Sydney, NSW: School of Geomatic Engineering, University of New South Wales, 1998.
Знайти повний текст джерелаS, 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.
Знайти повний текст джерелаЧастини книг з теми "Ionosphere Estimation"
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.
Повний текст джерелаBidikar, 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.
Повний текст джерелаYang, 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.
Повний текст джерелаAlken, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаXuguang, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаLingwal, 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.
Повний текст джерелаZhao, 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.
Повний текст джерелаТези доповідей конференцій з теми "Ionosphere Estimation"
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.
Повний текст джерелаAlves, 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.
Повний текст джерелаKunitsyn, 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.
Повний текст джерелаBagdasaryan, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаMiyake, 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.
Повний текст джерелаMaheshwari, 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.
Повний текст джерелаSamanes, 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.
Повний текст джерелаYue, 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.
Повний текст джерелаЗвіти організацій з теми "Ionosphere Estimation"
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.
Повний текст джерела