Some time ago, I posted an idea, on how the concept of a polynomial approximation can be simplified, in terms of the real-time Math that needs to be performed, in order to produce 4x oversampling, *in which the positions of the interpolated samples with respect to time, are fixed positions*.

In order for the reader to understand the present posting, which is a reiteration, he or she would need to read the posting I linked to above. Without reading that posting, the reader will not understand the Matrix, which I included below.

There was something clearly wrong with the idea, which I wrote above, but what is wrong, is not the fact that I computed, or assume the usefulness, of a product between two matrices. What is wrong with the idea as first posted above, is that the order of the approximation is only 4, thus implying a polynomial of the 3rd degree. This is a source of poor approximations close to the Nyquist Frequency.

But As I wrote before, the idea of using anything based on polynomials, can be extended to 7th-order approximations, which imply polynomials of the 6th degree. Further, there is no reason why a 7×7 matrix cannot be pre-multiplied by a 3×7 matrix. The result will only be a 3×7 matrix.

Hence, if we were to assume that such a matrix is to be used, this is the worksheet, which computed what that matrix would have to be:

The way this would be used in a practical application is, that a vector of input-samples be formed, corresponding to

t = [ -3, -2, -1, 0, +1, +2, +3 ]

And that the interpolation should result corresponding to

t = [ 0, 1/4, 1/2, 3/4 ]

Further, the interpolation at t = 0 does not need to be recomputed, as it was already provided by the 4th element of the input vector. So the input-vector would only need to be multiplied by the suggested matrix, to arrive at the other 3 values. After that, a new sample can be used as the new, 7th element of the vector, while the old 1st element is dropped, so that another 3 interpolated samples can be computed.

This would be an example of an idea which does not work out well according to a first approximation, but which will produce high-quality results, when the method is applied more rigorously.

Dirk

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