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Figures 26 and 27
show the same example as in Figures
and
.
What is new here is that the proper PEF
is not given but is determined from the data.
Figure 26 was made with a three-coefficient filter
and
Figure 27 was made with a five-coefficient filter
.
The main difference in the figures is where the data is sparse.
The data points in Figures
,
26 and
27 are samples from a sinusoid.
subsine3
Figure 26.
Interpolating with a three-term filter.
The interpolated signal is fairly monofrequency.
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subsine5
Figure 27.
Interpolating with a five term filter.
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Comparing Figures
and
to
Figures 26 and 27
we conclude that by finding and imposing
the prediction-error filter
while finding the model space,
we have interpolated beyond aliasing in data space.
Sometimes PEFs enable us to interpolate beyond aliasing.
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| Multidimensional autoregression | |
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Next: Analysis for leveled inverse
Up: LEVELED INVERSE INTERPOLATION
Previous: LEVELED INVERSE INTERPOLATION
2013-07-26