atmospheric_drag¶
Monte-Carlo sampling of errors due to atmospheric drag force uncertainty.
Estimate a power-law model of error standard deviation in along-track direction (largest error).
Juha Vierinen
Module summary¶
Functions
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Estimate position errors as a function of time, assuming a certain error in atmospheric drag. |
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Given pos vector, and pos vector at a small positive time offset, calculate unit vectors for along track, normal (towards center of Earth), and cross-track directions |
Contents¶
Functions
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sorts.errors.atmospheric_drag.atmospheric_errors(o, a_err_std=0.01, N_samps=100, plot=False, threshold_error=100.0, res=500)[source] Estimate position errors as a function of time, assuming a certain error in atmospheric drag.
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sorts.errors.atmospheric_drag.get_inertial_basis(ecef0, ecef0_dt)[source] Given pos vector, and pos vector at a small positive time offset, calculate unit vectors for along track, normal (towards center of Earth), and cross-track directions