THE AMBIGUITY DIAGRAM |
|
|
|
|
function [reference, convolution] = waveform2(pts, slope) % Waveform
#2 is a chirp signal reference = cos(2 * pi * (- (slope * pts) + slope * ([1:pts]')) .* ([1:pts]') / pts); % frequency
increases over time matchedir = reference; for i = 1:pts
matchedir(i) = reference(1 + pts - i); end % Matchedir
is the time-reversed version of reference frange = -5:0.01:5; convolution = []; for i = 1:length(frange) freq =
frange(i) - (slope * pts);
realsig = real(exp(j * 2 * pi * (freq + slope * ([1:pts]')) .* ([1:pts]')
/ pts));
imagsig = imag(exp(j * 2 * pi * (freq + slope * ([1:pts]')) .* ([1:pts]')
/ pts));
% Break signal into real and imaginary components
realconv = conv(realsig, matchedir);
imagconv = conv(imagsig, matchedir);
% Convolve each part individually
convolution = [convolution, sqrt((realconv .* realconv) + (imagconv .*
imagconv))];
% Build the convolution vector end ^ back to
top ^
|
MOTIVATION – Why this is important OBJECTIVE – What we hoped to achieve AMBIGUITY DIAGRAM - What it is AMBIGUITY DIAGRAM - How to read it WAVEFORMS – The signals we analyzed RESULTS – Results for CW and PCM CHIRP - A closer look POSSIBLE EXTENTIONS – What’s next CODE - Fascinating stuff ACKNOWLEDGMENTS - Who we have to thank |