Francesco Potorti` wrote:
I have a 200,000 samples measurement, which is a sort of noise with
bandwidth in the order of the inverse of 500 samples. Superimposed to
this noise there is a very slow sinusoid with a period of about
300,000
samples. This means that my measurements do not even see a complete
cycle of this sinusoid.
I want to estimate the sinusoid (in order to remove it from the
measurement). So I need some sort of very-low pass filter. However,
usign a simple causal low-pass filter would give me a delayed output.
Can anyone suggest some keyword for a noncausal filter to look for? I
guess that it could be simple, because the bandwidths of signal and
noise are so far away each other.
If the low-frequency signal is a 'clean' sinusoid, then I'd try
fitting a sinusoid to the data, and then subtract this sinusoid from
the data.
After some experimentation, it turns out that my low-frequency signal
is not a sinusoid. So I stuck with filtfilt. Now, I am deciding what
filter to use and how to choose its parameters.
Thank you
This book can perhaps be of some help for you:
Bretthorst, G. Larry, "Bayesian Spectrum Analysis and Parameter
Estimation"
http://bayes.wustl.edu/glb/book2.pdf
/Fredrik
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