Deconvolution of Two Datasets In signal analysis, deconvolution is the process of
subtracting or filtering out an unwanted signal. Initially the
worksheet has data in Time(X), Signal(Y) and Response(Y)
columns, with empty Index(disregard) and DeConv(Y) columns to
hold the results.
The Response dataset must be adjusted to have an odd
number of points such that twice the number of points is less than
the number of points in the signal dataset. This is the signal we will
try to remove (using deconvolution). For example, if the Signal
dataset has 1000 datapoints, the Response dataset should have 499
points (or less and an odd number of points).
The first step in using the deconvolution function is to
assign the dataset names in the fft object:
fft.deconv.indexdata$=Sample_Index;
fft.deconv.deconvdata$=Sample_DeConv;
Then we can call the deconvolution function with the signal
and response arguments:
fft.deconv(Sample_Signal,Sample_Response);
