Direct Density Correction Method: review of results

Yurasov, Vasiliy S.; Назаренко, А. И.; Alfriend, Kyle T.; Cefola, P. J.

doi:10.2514/6.iac-06-c1.5.02

Cited by 9 publications

(8 citation statements)

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“…We did not consider GPS receiver clock off-sets GONDELACH AND LINARES 10.1029/2020SW002620 Note. The BC values were taken from Bowman, Marcos, and Kendra (2004), Emmert, Picone, et al (2004), Yurasov et al (2005), Emmert, Meier, et al (2006), and Marcos et al (2006). although they may be present in the data.…”

Section: Gps Tracking Datamentioning

confidence: 99%

“…The drawback of these approaches using GPS data is that they require sophisticated precise orbit determination techniques or precision orbit ephemeris. Finally, two‐line element data, which is generated using radar and optical tracking data from the United States Space Surveillance Network, has been used by various authors to estimate densities (Cefola et al., 2004; Doornbos et al., 2008; Gondelach & Linares, 2020c; Picone, Emmert, & Lean, 2005; Yurasov et al., 2005). However, due to the limited accuracy and frequency of TLE data, the TLE data of many objects is needed to obtain densities with good accuracy and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

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Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Gondelach

Linares

2021

Space Weather

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Section: Gps Tracking Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Gondelach

Linares

2021

Space Weather

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“…This choice will guide the development of the estimation scheme. Some past efforts have used altitude-dependent corrections to the outputs of a Jacchia model 19 . Another parameterization makes corrections to the model inputs, for instance by specifying the coefficients of a spherical harmonic expansion for exospheric temperature 22 above.…”

Section: Spline-based Density Parameterizationmentioning

confidence: 99%

“…They do not have any unknown bias, but their noise covariance is generally much higher than the phase ranges. The pseudorange measurement equation takes the form (19) Once again the implicit constraint of Eq. (18) must be solved iteratively to determine the true time of transmission of the signal.…”

Section: B Measurement Modelsmentioning

confidence: 99%

“…Several calibration projects have recently demonstrated improved density knowledge and orbit determination performance in comparison with standard models. For example, some approaches take as their measurements the two-line elements (TLE's) of tracked space objects, which are made available by NORAD, and employ long histories of these measurements to calculate corrections to a density model 18,19,20,21 . Another method's calibration strategy aims to reduce the standard deviation of many months' worth of multi-day batch fits of a satellite's inverse ballistic coefficient 12,13 .…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Orbit and Atmospheric Density Estimation for a Satellite Constellation

Hinks

Psiaki

2010

AIAA/AAS Astrodynamics Specialist Conference

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A method is defined for simultaneous atmospheric density calibration and satellite orbit determination for a satellite constellation, and a linearized observability analysis is performed to evaluate the feasibility of the approach. Such an estimation scheme will provide data-based estimates of upper atmosphere density that improve on existing models, e.g. the Jacchia models, along with enhanced accuracy of the associated satellites' orbits. A new spline-based atmospheric density parameterization is developed that meshes well with the structure of the orbit determination problem and can be initialized so as to match the outputs of a traditional model. While conceptually similar to previous atmospheric calibration efforts, the proposed constellation approach restricts its global density estimates to the relevant satellite altitude range and thus reduces the complexity of the estimation problem. Measurements include dual-one-way-ranging between pairs of adjacent satellites in the same orbital plane, and carrier phase and pseudorange measurements between ground stations and satellites. Equations for a linearized observability analysis are derived and shown to be equivalent to the covariance calculations of an extended square-root information filter. In addition to the system observability evaluation, the impact of incorporating some a priori density information is explored. The results show that atmospheric density can be observed, but a reasonable amount of a priori information is necessary to obtain useful estimates. Degree of observability depends on the constellation configuration and dynamic model parameter values.

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Drag Coefficient Estimation in Orbit Determination

2011

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Drag modeling is the greatest uncertainty in the dynamics of low Earth satellite orbits where ballistic coefficient and density errors dominate drag errors. This paper examines fitted drag coefficients found as part of a precision orbit determination process for Stella, Starlette, and the GEOSAT Follow-On satellites from 2000 to 2005. The drag coefficients for the spherical Stella and Starlette satellites are assumed to be highly correlated with density model error. The results using MSIS-86, NRLMSISE-OO, and NRLMSISE-OO with dynamic calibration of the atmosphere (DCA) density corrections are compared. The DCA corrections were formulated for altitudes of 200-600 kIn and are found to be inappropriate when applied at 800 kIn. The yearly mean fitted drag coefficients are calculated for each satellite for each year studied. The yearly mean drag coefficients are higher for Starlette than Stella, where Starlette is at a higher altitude. The yearly mean fitted drag coefficients for all three satellites decrease as solar activity decreases after solar maximum.

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Direct Density Correction Method: review of results

Cited by 9 publications

References 1 publication

Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Simultaneous Orbit and Atmospheric Density Estimation for a Satellite Constellation

Drag Coefficient Estimation in Orbit Determination

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