The following works pretty well under low noise conditions and fast sampling and may do depending on your requirements for accuracy. It uses a simple threshold and thus is subject to inaccuracy when things get noisy. Adjust thresh
to a low value above the noise.
Nwav = 3;
Np = 100;
tmax = 50;
A = 1000;
Nz = Np/5;
%%%%%%%%%%%%%%
thresh = A/50;
%%%%%%%%%%%%%%
% generate some waveforms
t = [0:tmax/(Np-1):tmax].';
w = rand(1,Nwav);
offs = round(rand(1,Nwav)*100);
sig = [A*sin(t(1:end-Nz)*w) ; zeros(Nz,Nwav)] + randn(Np,Nwav);
for ii=1:Nwav
sig(:,ii) = circshift(sig(:,ii),round(rand()*Nz));
end
figure, plot(t,sig)
hold on, plot(t,repmat(thresh,length(t),1),'k--')
% use the threshold and align the waveforms
for ii=1:Nwav
[ir ic] = find(sig(:,ii)>thresh,1)
sig(:,ii) = circshift(sig(:,ii),-ir+1);
end
figure, plot(t,sig)
hold on, plot(t,repmat(thresh,length(t),1),'k--')
There is room for improvement (noise filtering, slope detection) but this should get you started.
I also recommend you look into waveform processing toolboxes, in matlab central for instance.