De-noising an accelerometer with autoregression

In summary, the speaker is asking for advice on denoising a signal using autoregression after filtering high frequency noise. They have tried using a simulation and subtracting it from the noisy signal, but they are now experiencing a problem with the increased standard deviation of the signal. They are unsure if this is the correct approach and are asking for clarification on whether to simulate the auto-regression process or use a transfer function. The speaker also apologizes for their lack of familiarity with signal processing.
  • #1
ramesses
17
0
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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  • #2
It's a tough problem. Can you show an example graph or something like that?
 
  • #3
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
 

1. What is an accelerometer?

An accelerometer is a sensor that measures the acceleration of an object in one or more directions. It is commonly used in scientific research and in devices such as smartphones and fitness trackers to measure movement and orientation.

2. Why is de-noising an accelerometer important?

Accelerometer signals can be affected by noise, which can distort the data and make it difficult to accurately interpret. De-noising techniques are used to remove this noise and improve the quality of the data.

3. What is autoregression?

Autoregression is a statistical method used to model time series data, such as accelerometer signals. It involves predicting the value of a data point based on its previous values.

4. How does autoregression help with de-noising an accelerometer?

Autoregression can be used to identify and remove noise from accelerometer signals by modeling the noise as a separate time series and subtracting it from the original data.

5. What are some limitations of de-noising an accelerometer with autoregression?

One limitation is that it assumes the noise is stationary, meaning it has the same statistical properties throughout the data. It may also be sensitive to outliers and may not be effective for non-linear relationships between the signal and noise.

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