signal processing - Robust least squares in Octave -


i programming classical prony's method (or prony's analysis) using octave. script works pretty fine on test signal such as:

t = 1:600; sig = exp(-0.01*t).*cos(0.1*t);

but totally collapses when small amount of noise added:

sig = sig + 0.001*rand(size(sig));

the issue computing overdetermined set of equations using pinv when linear prediction bulit (see link above). need more robust , less sensitive outliers. sadly, no function in octave core helped me far. have tried ols , lscov don't trick.

any hints, please?

note: aware classic prony problematic because of issues , there modified algorithms (such this one). feel haven't done maximum classic method work. using better solver should persist kind of noise.


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