Polynomial Regression filter implementation
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How can I implement Polynomial Regression filter for clutter filtering?
I would appreciate if you give me an example to understand it.
Best
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Respuestas (3)
Bjorn Gustavsson
el 24 de Mayo de 2016
First have a look at
sgolayfilt
If you need to filter images and it's not enough to apply sgolayfilt along either direction you might possibly be ably to make a version of sgolayfilt for 2-D that uses polyfitn, in the worst case you could make a function that simply loops around using polyfitn.
HTH
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Star Strider
el 25 de Mayo de 2016
I’m listing this as an Answer so I can find it again. If I have the opportunity to code and test it, I’ll follow up. I’m not promising anything.
A good discussion: Ground Clutter Canceling with a Regression Filter Journal of Atmospheric and Oceanic Technology October 1999.
This reference Polynomial regression filters and Calculation of time-variant Magnitude Responses are everything I can find on it.
The legendre function seems to me to be able to produce the polynomials, but I would have to know much more about it to know if it could be applied here.
There is nothing in the File Exchange, and no MATLAB code for it online.
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mireirei
el 1 de Dic. de 2016
has anyone figured out how to implement poly reg filter? I'm looking for a file exchange source file if possible.....
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Miftah Bedru
el 20 de Oct. de 2022
Editada: Miftah Bedru
el 15 de Dic. de 2022
I know it will be too late by the time you see this post , I myself was looking for such implementation and all what I found was papers and some bolgs ... below are best resources in terms of sample code and quick material to read :
To get more detail resources just click here . Sorry for just putting links. I saw the discussion and the code are better elaborated in the link. In case the above link get broken in the futue , here I put short code segement shared to me by a good friend regarding ploy filter that uses matrxi implemntation of polynomial filter ( I am assuiming ultrasound data/signal here )
SIG = reshape(shiftdim(SIG,2),N,[]); % SIG refers a 3D signal slow-time, fast timem and channel - signal reshaped for better application of the below filter coeff.
t = linspace(0,1,N).'; %% time span between each shot along the slow time
V(:,n+1) = ones(N,1); %%% Vandermode matrix to hold orthogonla polynomilas which will be fitted to the clutters
for k = n:-1:1
V(:,k) = t.*V(:,k+1); %%
end
A = eye(N)-V*pinv(V); #### creating filtering coeffcient using the formulata y = I - b.b'
SIG = A*SIG; %%% APPLYING THE FILTER TO THE SIGNAL
SIG = reshape(permute(SIG,[3 2 1]),siz0); %%% Reshape the filtered signal for display .
It may not be the best but I am just sharing what I got while sniffing around ...
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