De-noising an accelerometer with autoregression

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SUMMARY

The discussion centers on denoising accelerometer signals using autoregression after filtering high-frequency noise, specifically employing the Burg method for noise analysis. The user encountered an issue where the standard deviation of the denoised signal increased, raising questions about the appropriateness of their denoising approach. They sought clarification on whether to simulate the autoregression process before subtracting it from the original signal or to utilize a transfer function for denoising.

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  • Understanding of autoregressive models in signal processing
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  • Basic concepts of standard deviation and its implications in signal analysis
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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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