Statistical Approach to Basis Function Truncation in Digital Interpolation Filters
In this paper an alternative analysis in the time
domain is described and the results of the interpolation process are
presented by means of functions that are based on the rule of
conditional mathematical expectation and the covariance function. A
comparison between the interpolation error caused by low order
filters and the classic sinc(t) truncated function is also presented.
When fewer samples are used, low-order filters have less error. If the
number of samples increases, the sinc(t) type functions are a better
alternative. Generally speaking there is an optimal filter for each
input signal which depends on the filter length and covariance
function of the signal. A novel scheme of work for adaptive
interpolation filters is also presented.
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