Simulating noise with Yuler and Burg

AI Thread Summary
The discussion centers on simulating low-frequency noise using Burg or Yuler methods after removing high-frequency white noise from an INS sensor. The user encounters issues with generated sequences diverging and producing large values, questioning if this behavior is typical. They reference an equation for their simulation and express confusion about the necessity of using a Kalman filter for error estimation instead of directly integrating simulated values. The conversation highlights the complexity of the mathematical model involved, suggesting that clearer notation might facilitate better responses. Overall, the thread emphasizes challenges in noise simulation and the integration of mathematical concepts in practical applications.
ramesses
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Hi
After removing high frequency white noise in my INS sensor, I want to simulate low frequency noise with Burg or Yuler.
I used some functions in python to analyse the recorded data and to get parameters to use.
But the the problem is the sequences generated diverge (and take very big values), is that normal ?
I use the following equation :
XN+4=XN+3*C3+XN+2*C2+XN+1*C1+W1
where W1 is white noise.
I have another question.
Why I have to use kalman filter to estimate the error ?
Why not to integrate directly the value given by the simulation ?
 
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I have no idea what you wrote. Is that English? :bow:

It seems to be some sort of auto-regressive model, but I don't recognize the math. Perhaps if that were clearer (use that little Σ button if it helps) more people would respond.

Also another forum might find more people familiar with this sort of math.
 
English is my 3rd language :wink:
XN=Σ3i=0Xi*Ci +W1
 
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