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the_dialogue
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I'm trying to understand the Wiener Filter, and I have a few questions.
1. How can there be such a thing as a correlation matrix of 1 vector. I read here:
R_yy = E[ y(k) * y^T(k) ]
where y(k) is a vector, and y_T(x) is the same vector transposed. I thought correlation represents the degree of correspondence between 2 variables, so how can we say there is a correlation between 2 equivalent vectors?
2. They present a matrix R_yy as the correlation matrix (mentioned above). Then they say vector "r_yy" is the first column of R_yy. If that is so, what are the other columns of R_yy?
3. The book first presents the Wiener filter formulation by saying that in minimizing the MSE criterion, we can find "h_w" (the wiener filter vector). They go on to another form of the Wiener filter by saying:
h_w = h_1 - inv(R_yy)*r_vv
where h_1 was defined as [1 0 0 0 ...]^T and r_vv, I suppose is the correlation (once again I don't know how one can have correlation between 2 equivalent vectors).
Thank you for any help.
1. How can there be such a thing as a correlation matrix of 1 vector. I read here:
R_yy = E[ y(k) * y^T(k) ]
where y(k) is a vector, and y_T(x) is the same vector transposed. I thought correlation represents the degree of correspondence between 2 variables, so how can we say there is a correlation between 2 equivalent vectors?
2. They present a matrix R_yy as the correlation matrix (mentioned above). Then they say vector "r_yy" is the first column of R_yy. If that is so, what are the other columns of R_yy?
3. The book first presents the Wiener filter formulation by saying that in minimizing the MSE criterion, we can find "h_w" (the wiener filter vector). They go on to another form of the Wiener filter by saying:
h_w = h_1 - inv(R_yy)*r_vv
where h_1 was defined as [1 0 0 0 ...]^T and r_vv, I suppose is the correlation (once again I don't know how one can have correlation between 2 equivalent vectors).
Thank you for any help.