Dear Shada,

Kalman filtering may be one of the most suitable denoising filters.

But i would urge you first to analyse your signal by FFT and DWT and see the range of the frequencies of representing your noise in specific signal blocks.

That you make signal analysis in order to get the best method to reduce the noise from the signal.

Best wishes

During the last time the wavelets have become a popular de-noising (or noise reduction) tool. Many algorithms define a criterion to divide wavelet transform coefficients into two groups. The first group contains the coefficients dominated by a noise, while other coefficients are rather clean. These algorithms eliminate all wavelet coefficients below a certain threshold, because these coefficients are dominated by a noise.

The wavelet based filtering is the best solution so far. You can find it in Matlab simulink.

For real-time noise reduction you can to use the Wavelet functions as Daubechies Wavelets

Thank you all, I am looking into wavelet based filtering and it seems fit for my application. I appreciate your time

The best is Kalman Filtering, Butterworth Filtering, and not the less is Grassmann Filtering, see for example : Preprint An overview on Optimal Kalman and Grassmann Filtering Techni...

Dear Shada,

Kalman filtering may be one of the most suitable denoising filters.

But i would urge you first to analyse your signal by FFT and DWT and see the range of the frequencies of representing your noise in specific signal blocks.

That you make signal analysis in order to get the best method to reduce the noise from the signal.

Best wishes

For a real time solution

that can go up to any

frequency please read

my paper

Construction of The Sampled Signal Up To Any Frequency While Keeping The Sampling Rate Fixed