midgard.gnss

midgard.gnss.solution_validation

comp_quality_indicators()

Full name: midgard.gnss.solution_validation.comp_quality_indicators

Signature: (sol_vc_mat:numpy.ndarray) -> tuple

Compute quality indicators

Following quality indicators are computed 1. compute the standard error ellipse(SEE) 2. compute the distance root mean squared (DRMS) 3. compute the circular error probable (CEP)

Args:

Returns:

Tuple with DRMS, CEP and SEE

compute_dops()

Full name: midgard.gnss.solution_validation.compute_dops

Signature: (az:numpy.ndarray, el:numpy.ndarray, el_mask:float=0, min_num_sats:int=4) -> Tuple[numpy.ndarray, ...]

Compute DOP (dilution of precision)

Args:

Returns:

Tuple with GDOP, PDOP, HDOP and VDOP

epilog (str)

epilog = '\n**EXAMPLE**\n sol_validation (residuals, alpha_sign_level n_params)\n args:\n residuals (I): postfit residuals \n alpha_sign_level(I): alpha significance level and defines the rejection area.\n possible values of alpha = 0.05 (95%), 0.01 (99%) and 0.001 (99.9%)\n n_params (I): number of estimated parameters (states).\n \n\nKeywords: Chi-square distribution,\n'

get_my_parser()

Full name: midgard.gnss.solution_validation.get_my_parser

Signature: ()

main()

Full name: midgard.gnss.solution_validation.main

Signature: ()

Main program for testing solution validation implementation

TODO: This should be done via midgard/tests/gnss !!!

prolog (str)

prolog = '\n**PROGRAM**\n solution_validation.py\n \n**PURPOSE**\n Perform Chi-square test for residuals. Degrees of freedom (df) refers to the number of values that\n are free to vary df = number of valid satellites (nv) - number of parameters to be estimated (nx) - 1.\n GNSS solution validation based on the argument alpha, the level of significance (e.g. 99%), and\n defines the rejection level of the crossing events. \n Note that this is different from the false alarm rate, which instead refers to error type I\n \n**USAGE**\n'

sol_validation()

Full name: midgard.gnss.solution_validation.sol_validation

Signature: (residuals:numpy.ndarray, alpha_siglev:float, n_params:int=4) -> bool

Validating the GNSS solution is carried out using Chi-square test

Use Chi-square test for outlier detection and rejection.

Args:

Returns:

Array containing False for observations to throw away.