Further noise reduction techniques ontop of Savitzky-Golay FIR filter

In summary, The conversation discusses the design of a prototype for a thesis that will read in sensory data wirelessly to a MATLAB script. The data contains noise but also important information in the high frequency components. The use of a Savitzky-Golay FIR filter is proposed, but there is discussion about possibly using additional noise reduction techniques while preserving the high frequency component. Suggestions such as a Kalman filter, a specific LP filter, and a Wiener filter are mentioned. The Wiener filter is suggested as the theoretically optimum option for preserving SNR at the output.
  • #1
Ian_Brooks
129
0
Hi PF Designers,

Wasn't too sure if this would belong in the Electrical engineering forum but here goes.

I'm designing a prototype for my thesis that will read in sensory data wirelessly to a MATLAB script. This sensory data is riddled with noise, however important data is contained in the High frequency components of the signal as well.

Hence I was naturally going to use a Savitzky-Golay FIR filter in matlab. However I was wondering if I could perform further noise reduction techniques on top of this and still preserve the high frequency component that I saved.

Thoughts, dilemmas, discussion, tid bits of help?
 
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  • #2
Do you have any theoretical model?

If so there are many thechniques (e.g. kalman filter).
http://en.wikipedia.org/wiki/Kalman_filter

If not, instead of using S-golay, you can try building a specific LP filter and play with the threshold frequency
 
  • #3
If you know the form of your input data, then a Wiener filter will give you the theoretically optimum SNR at the output. For white Gaussian noise you can implement the optimal filter as a matched filter.
 

1. What is a Savitzky-Golay FIR filter?

A Savitzky-Golay FIR filter is a digital signal processing technique used for smoothing data. It works by fitting a polynomial function to a small window of data and using that function to estimate the smoothed value at the center of the window.

2. How does a Savitzky-Golay FIR filter reduce noise?

The polynomial function used in a Savitzky-Golay FIR filter acts as a smoothing function, removing high frequency noise from the data. It also takes into account neighboring data points, allowing for better noise reduction than simple moving average techniques.

3. Are there limitations to using a Savitzky-Golay FIR filter for noise reduction?

Yes, there are some limitations to using a Savitzky-Golay FIR filter for noise reduction. It works best for removing random noise, but may not be as effective for removing periodic or systematic noise. The effectiveness also depends on the choice of filter parameters and the characteristics of the data being filtered.

4. What are some additional techniques that can be used in conjunction with a Savitzky-Golay FIR filter for further noise reduction?

Some additional techniques that can be used in conjunction with a Savitzky-Golay FIR filter include wavelet denoising, Kalman filtering, and adaptive filtering. These techniques can help to further reduce noise and improve the overall quality of the filtered data.

5. Is there a trade-off between noise reduction and preserving the original signal when using additional techniques on top of a Savitzky-Golay FIR filter?

Yes, there is often a trade-off between noise reduction and preserving the original signal when using additional techniques on top of a Savitzky-Golay FIR filter. Some techniques may be more aggressive in reducing noise but may also result in a more distorted signal. It is important to carefully choose the appropriate combination of techniques based on the specific needs and goals of the data analysis.

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