Improved description.

applications/apertif/matlab/correlator_null_clip_or_wrap.m

@@ -24,9 +24,14 @@
 %   
 % Description :
 %   Clipping seems preferrable because it does still contribute somewhat
-%   constuctive to the correlation. Even a little bit (ampl = 1.1) of
-%   wrapping already gives seemingly random output, so one could even
-%   argue that wrapping does not cause decorrelation. 
+%   constuctive to the correlation. Clipping preserves the phase
+%   information (zero crossings) of the signal and a clipped signal still
+%   resembles the original overflow signal.
+%   Even a little bit (ampl = 1.1) of wrapping already gives seemingly
+%   random output. For severe overflow (ampl > 2) the wrapping cause extra
+%   zero crossings. If the wrapped signal becomes sufficiently random
+%   then the contribution of wrapped parts of the signal to the
+%   correlation will be close to zero.
 %   Using noise input it shows that for clipped input the correlator
 %   output still follows the correlator output of the full scale input
 %   that is used as reference (ref).
@@ -34,10 +39,15 @@
 %   correlator output can be normalized by N or by the number of non-zero
 %   contributions N-cnt. The plots show that nulling and scaling yield
 %   somewhat destructive correlator output.
+%   Even without RFI the signal may occasionally (e.g. ampl = 1.01)
+%   overflow due to the variation of the noise. For this clipping seems
+%   the most graceful choice.
 %
 %   Conclusion: best choose input clipping for a correlator.
 %
-%   Remark:
+%   Remarks:
+%   . This model does not investigate the effect of the channel filterbank
+%     on the data.
 %   . Delay tracking and fringe stopping will take care that the input 
 %     b will be in phase with input a, so should we in the plots only
 %     look at the correlator output for phase difference close to 0 or 
@@ -59,7 +69,7 @@ close all;
 %rng(0);    % random seed for repeatable result
 
 % Try different amplitudes
-ampl = 1.5;  % for input rand() or sin() use > 1 for overflow
+ampl = 8;  % for input rand() or sin() use > 1 for overflow
              % for input randn() ampl = sigma, so use >~ 0.3 for overflow
 
 N = 1000;