Further noise reduction techniques ontop of Savitzky-Golay FIR filter

AI Thread Summary
The discussion focuses on enhancing noise reduction techniques beyond the Savitzky-Golay FIR filter for processing sensory data in MATLAB. The original poster seeks methods to maintain high-frequency components while reducing noise. Suggestions include using a Kalman filter, a specific low-pass filter with adjustable threshold frequency, or a Wiener filter for optimal signal-to-noise ratio. The conversation emphasizes the importance of knowing the input data characteristics to choose the best filtering approach. Overall, the goal is to achieve effective noise reduction without losing critical high-frequency information.
Ian_Brooks
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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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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
 
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.
 
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