Comparative studies of atmospheric density models used for earth satellite orbit estimation

Shanklin, R. E.; Lee, T.; Samii, M. V.; Mallick, Mahendra; Cappellari, James O.

doi:10.2514/3.8572

Cited by 4 publications

(2 citation statements)

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“…Especially for LEO satellites, atmosphere drag would cause considerable orbit decay and it is the main error source in orbital prediction. Most empirical atmospheric density models [25], such as Jacchia [26], NRLMSISE [27], DTM [28], Jacchia-Bowman 2006 [30] and Russian GOST [31] combine theoretical computation with drag measurements of satellites. Their accuracy is much better than the traditional exponential one, but still no better than 10%~30%.…”

Section: Introductionmentioning

confidence: 99%

Correction of Atmospheric Model Through Data Mining With Historical Data of Two-Line Element

Bai

Liao

et al. 2020

IEEE Access

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The existing atmospheric mass density models (AMDM) would produce considerable errors in orbital prediction for Low Earth Orbit (LEO) satellites. In order to reduce these errors and correct the AMDM, this paper presents methods based on data mining with historical data of two-line element (TLE). Starting from a typical LEO satellite, TIANHUI, two orbital dynamical models are firstly proposed as the simulation environment to generate training data. The historical TLE data are regarded as actual space environment and used to generate application data. Secondly, three data mining methods, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), are combined with the training data to investigate their feasibility in recovering the known deviation of AMDM under simulation environment. Training results show that RF displays the best performance and achieves the accuracy of 99.99%, while the other two methods only achieve 86.83% and 71.90% respectively. Thirdly, under the actual space environment, this paper uses new training and application data to research the ability of the three methods in recovering the unknown deviation of the AMDM and improve the accuracy of orbital prediction. Numerical results are evidential to the accuracy of the proposed methods based on data mining. It is concluded that the capabilities of the data mining for correction for the atmospheric model are very promising, with great potential to advance practical applications on on-orbit propagation.

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Section: Introductionmentioning

confidence: 99%

Correction of Atmospheric Model Through Data Mining With Historical Data of Two-Line Element

Bai

Liao

et al. 2020

IEEE Access

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“…It cannot be used in high-accuracy studies. In order to improve the accuracy, the empirical density models [1], such as Jacchia [2], NRLMSISE [3], DTM [4], and Russian GOST [5] are found by combining theoretical computation with measurements of satellite drag [6]. Their accuracies are much better than that of the exponential model, but still no better than 10%-30% [7,8].…”

Section: Introductionmentioning

confidence: 99%

Calibration of atmospheric density model using two-line element data

Ren

Shan

2017

Astrodyn

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For satellites in orbits, most perturbations can be well modeled; however the inaccuracy of the atmospheric density model remains the biggest error source in orbit determination and prediction. The commonly used empirical atmospheric density models, such as Jacchia, NRLMSISE, DTM, and Russian GOST, still have a relative error of about 10%-30%.Because of the uncertainty in the atmospheric density distribution, high accuracy estimation of the atmospheric density cannot be achieved using a deterministic model. A better way to improve the accuracy is to calibrate the model with updated measurements. Twoline element (TLE) sets provide accessible orbital data, which can be used in the model calibration. In this paper, an algorithm for calibrating the atmospheric density model is developed. First, the density distribution of the atmosphere is represented by a power series expansion whose coefficients are denoted by the spherical harmonic expansions. Then, the useful historical TLE data are selected. The ballistic coefficients of the objects are estimated using the BSTAR data in TLEs, and the parameterized model is calibrated by solving a nonlinear least squares problem. Simulation results show that the prediction error is reduced using the proposed calibration algorithm. KEYWORDS atmospheric density model two-line element (TLE)Research Article

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Determination of momentum accommodation at satellite altitudes usinga new set of coefficients

Crowther

Stark

1989

27th Aerospace Sciences Meeting

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Comparative studies of atmospheric density models used for earth satellite orbit estimation

Cited by 4 publications

References 1 publication

Correction of Atmospheric Model Through Data Mining With Historical Data of Two-Line Element

Correction of Atmospheric Model Through Data Mining With Historical Data of Two-Line Element

Calibration of atmospheric density model using two-line element data

Determination of momentum accommodation at satellite altitudes usinga new set of coefficients

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