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

Gondelach, David J.; Linares, Richard

doi:10.1029/2020sw002620

Cited by 18 publications

(13 citation statements)

References 40 publications

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“…The high‐frequency density estimates, corrected for the drag‐coefficient biases with our proposed framework, can be a useful data source for such a method. Gondalech and Linares (2021) estimated the ballistic coefficient simultaneously with the local densities in their data‐assimilation technique. But the ballistic coefficient biases had to be calculated through several‐day estimation runs and corrected for the density biases.…”

Section: Discussionmentioning

confidence: 99%

“…This can be remedied by using reduced‐order models to represent them with a smaller subset of parameters (Mehta & Linares, 2017, 2018). This technique has been used to demonstrate the estimation of global atmospheric density by assimilating measurements from accelerometers (Mehta et al., 2018), two‐line element data (Gondalech & Linares, 2020), and radar and GPS measurements (Gondalech & Linares, 2021). All such data‐assimilation methods significantly improve global atmospheric density estimates over existing semi‐empirical models.…”

Section: Introductionmentioning

confidence: 99%

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A Framework to Estimate Local Atmospheric Densities With Reduced Drag‐Coefficient Biases

et al. 2022

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An accurate estimation of upper atmospheric densities is crucial for precise orbit determination (POD), prediction of low Earth orbit satellites, and scientific studies of the Earth's atmosphere. But densities estimated using satellite tracking data are always uncertain up to the drag‐coefficient assumed in the inversion method. This work develops a new framework to simultaneously estimate the density and drag‐coefficient for satellites with a time‐varying attitude. We do so by leveraging Fourier drag‐coefficient models, previously developed by the authors, and physical models of the drag‐coefficient. The method is tested with synthetic data for different geomagnetic activities, altitude levels, and errors in the gas‐surface interaction parameters. We report an improvement of up to 70% in density estimates for the simulations. Finally, POD data from Spire satellites are used for validation. An improvement of around 29% is obtained in the filter density estimates over NRLMSISE‐00 and 49% over JB2008 compared to the High Accuracy Satellite Drag Model densities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework to Estimate Local Atmospheric Densities With Reduced Drag‐Coefficient Biases

et al. 2022

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“…(2018) used PCA on TIE‐GCM data and developed a dynamic ROM using DMD with control (or DMDc). This approach has been applied by Gondelach and Linares (2020, 2021) on the NRLMSISE‐00, JB2008, and TIE‐GCM models with the goal of data assimilation. DMDc is based on the assumption of the linear relationship between successive time steps and the processes that drive the system,

\begin{array}{r}\hfill {\mathbf{x}}_{k+1}={\mathbf{A}\mathbf{x}}_{k}+{\mathbf{B}\mathbf{u}}_{k}\end{array}

where x denotes the state, A is the dynamic matrix, and B is the input matrix relating the system inputs/drivers and successive state of the system.…”

Section: Methodsmentioning

confidence: 99%

Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models

Licata

Mehta

2023

Space Weather

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The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE‐GCM ROPE has similar error to previous linear approaches while improving storm‐time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE‐GCM ROPE can capture the position resulting from TIE‐GCM density with <5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7–18 km.

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“…This work was extended using accelerometer-derived density estimates from the Challenging Minisatellite Payload (CHAMP) and Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) [96] satellites and TLE data [97] for model calibration. Gondelach and Linares [98] showed that a ROM model with TLE-estimated densities provides more precise Orbit Determination than the NRLMSISE-00 and JB2008 models, especially in the along-track coordinate. This ROM approach was improved upon using an anto-encoder for dimensionality reduction [99].…”

Section: Thermospheric Density Mass Modelsmentioning

confidence: 99%

Machine Learning in Orbit Estimation: a Survey

Caldas¹,

Soares²

2022

Preprint

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Since the late '50s, when the first artificial satellite was launched, the number of resident space objects (RSOs) has steadily increased. It is estimated that around 1 Million objects larger than 1 cm are currently orbiting the Earth, with only 30,000, larger than 10 cm, presently being tracked. To avert a chain reaction of collisions, termed Kessler Syndrome [1], it is indispensable to accurately track and predict space debris and satellites' orbit alike. Current physics-based methods have errors in the order of kilometres for 7 days predictions, which is insufficient when considering space debris that have mostly less than 1 meter. Typically, this failure is due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, as well as specific unknown characteristics such as mass or geometry of the RSO. Leveraging datadriven techniques, namely machine learning, the orbit prediction accuracy can be enhanced: by deriving unmeasured objects' characteristics, improving non-conservative forces' effects, and by the superior abstraction capacity that Deep Learning models have of modelling highly complex non-linear systems. In this survey, we provide an overview of the current work being done in this field.

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

Cited by 18 publications

References 40 publications

A Framework to Estimate Local Atmospheric Densities With Reduced Drag‐Coefficient Biases

A Framework to Estimate Local Atmospheric Densities With Reduced Drag‐Coefficient Biases

Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models

Machine Learning in Orbit Estimation: a Survey

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