De-noising an accelerometer with autoregression

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Denoising an accelerometer signal using autoregression after filtering high-frequency noise can lead to increased standard deviation, indicating potential issues in the denoising process. The user applied the Burg method for noise analysis and simulated autoregression but is uncertain about the correctness of their approach. They seek clarification on whether to simulate the autoregression process before subtraction or to use a transfer function for denoising. The discussion highlights the complexity of signal processing and the need for proper application of techniques. Further examples or visual aids could enhance understanding and application of the methods discussed.
ramesses
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Hi
I want to ask about denoising accelerometre with autoregression after filtring high frequency noise. I analysed the noise with Burg method.
To denoise the signal, I made a simulation with autoregression and subtracted it from the noised signal.
Now, I have a problem. The std of the signale become bigger.

What does it mean ? or this is a wrong way to denoise a signal ?
 
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It's a tough problem. Can you show an example graph or something like that?
 
I'm not sure that I applied correctly the filter.

do I have to simulate the process the auto-regression, then subtract the simulation for the original signal.

or use the transfer function like that : Y(n) =S(n)*1/(1+Σiαn-iY(n-i)) , where S(n) is the original signal ?

Sorry, I'm not very familiar with signal processing
 
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