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Practical stuff
OH today 12-1:30; also Friday
Never debug longer than 30 minutes
Ask for help! (from me or your classmates)
Reminder: GPS facts to memorize
Phase can be measured to precision of 1mm (or better)
L1 ~ 1.5 GHz, L2 ~ 1.2 GHz
Ionosphere delay ~ TEC/f2
The goal of data weighting is to make your uncertainty estimates meaningful (and your estimates more accurate). To do that, you need to remember the rules of least squares:
Assumptions of least squares:
1. you have a model that describes the observations (data)the observations are linearly related to the model
2. postfit residuals are zero mean and randomly distributed
3. you should know your observation errors before you start (or iterate when you do know them).
But if the problem is intrinsic to your data, you should pick a weight function that corresponds to the distribution of your residuals
Options:
If the non-gaussian distribution of the residuals is caused by a model defect, you can (and should) improve your model.
How do we solve the LS problem ?
Model: pfr = C/sineE
The partial of the pfr with respect to C is 1/sineE
A =[1./sind(angles)]; (least squares) C = A\pfr
Does weighting the data change the solution?
1. If weights are constant (i.e. same for all data), no.
2. If weights are not constant, the answers are different.