Variance-Covariance Modeling of Atmospheric Errors for Satellite-Based Network Positioning

Radovanovic, R. S.; El‐Sheimy, Naser; Teskey, W. F.

doi:10.1002/j.2161-4296.2004.tb00348.x

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…2) Tropospheric error model: [23], [26] The troposphere is the layer of atmosphere closest to the Earth's surface, comprised from the earth surface to 8/15 km. The variations in tropospheric delay are basically caused by the changing humidity, temperature and atmospheric pressure.…”

Section: Sources Of Errors and Error Modelsmentioning

confidence: 99%

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Comparison of MEO, LEO, and Terrestrial IoT Configurations in Terms of GDOP and Achievable Positioning Accuracies

Ferre

Lohan

2021

IEEE J. Radio Freq. Identif.

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Complementary solutions to the Medium Earth Orbit (MEO) Global Navigation Satellite Systems (GNSS) are more and more in demand to be able to achieve seamless positioning worldwide, in outdoor as well as in indoor scenarios, and to cope with increased interference threats in GNSS bands. Two of such complementary systems can rely on the emerging Low Earth Orbit (LEO) constellations and on the terrestrial long-range Internet of Things (IoT) systems, both under rapid developments nowadays. Standalone positioning solutions based on such systems complementary to GNSS can be beneficial in situations where GNSS signal is highly affected by interferences, such as jammers and spoofers, while hybrid GNSS and non-GNSS solutions making use of LEO and terrestrial IoT signals as signals of opportunity can improve the achievable positioning accuracy in a wide variety of scenarios. Comparative research of performance bounds achievable through MEO, LEO, and terrestrial IoT signals are still hard to find in the current literature. It is the goal of this paper to introduce a unified framework to compare these three system types, based on geometry matrices and error modeling, and to present a performance analysis in terms of Geometric Dilution of Precision (GDOP) and positioning accuracy bounds.

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Section: Sources Of Errors and Error Modelsmentioning

confidence: 99%

“…The tropospheric error is usually on the range of a few centimeters [27], [24], [23]. Examples of tropospheric error variance models can be found in [23], [28]. During our simulations we did not specifically model the tropospheric error, but we rather combined it jointly with the clock and orbit errors (see also Sec.…”

Section: Sources Of Errors and Error Modelsmentioning

confidence: 99%

Comparison of MEO, LEO, and Terrestrial IoT Configurations in Terms of GDOP and Achievable Positioning Accuracies

Ferre

Lohan

2021

IEEE J. Radio Freq. Identif.

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“…More recent examples of VCE studies dealing with the GNSS stochastic model are Tiberius and Borre (2000); Tiberius and Kenselaar (2000); Tiberius and Kenselaar (2003); Bona (2000); Wang et al (2002); Satirapod et al (2002); Radovanovic et al (2004); Bischoff et al (2005Bischoff et al ( , 2006, and Amiri-Simkooei and . Also in the GNSS position domain, similar studies are ongoing.…”

Section: Introductionmentioning

confidence: 99%

Least-squares variance component estimation

Teunissen

Amiri-Simkooei

2007

J Geod

283

148

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Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a userdefined weight matrix; and it is attractive because it allows one to directly apply the existing body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE. We also show how the existing body of knowledge of LS theory can be used to one's advantage for studying various aspects of VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected by the results of LS-VCE. Various examples are given to illustrate the theory.

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GPS/GNSS Current Bibliography

Soler

2004

GPS Solutions

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Variance-Covariance Modeling of Atmospheric Errors for Satellite-Based Network Positioning

Cited by 4 publications

References 10 publications

Comparison of MEO, LEO, and Terrestrial IoT Configurations in Terms of GDOP and Achievable Positioning Accuracies

Comparison of MEO, LEO, and Terrestrial IoT Configurations in Terms of GDOP and Achievable Positioning Accuracies

Least-squares variance component estimation

GPS/GNSS Current Bibliography

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