How to deconvolute Gaussian peaks with known locations and heights

4 次查看（过去 30 天）

Irem Altan 2021-8-24

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1439849-how-to-deconvolute-gaussian-peaks-with-known-locations-and-heights

评论： Irem Altan 2021-8-24

I have a signal with 9 or 10 peaks. I can get the peak locations by fitting a smoothing spline to the data and then using the findpeaks function. The result looks like this:

Now, I would like to deconvolute this signal by fitting a Gaussian to each peak. I am aware that the tallest peak probably has another peak to its right, and I'm smoothing too much to capture it, but I will fine tune the details later.

Since the cftool's Gaussian fitting is limited to 8 Gaussians, I create a custom fit with the function:

fittingFunc = 'a1*exp(-((x-477.73)/c1)^2)+a2*exp(-((x-489.143)/c2)^2)+a3*exp(-((x-506.111)/c3)^2)+a4*exp(-((x-542.774)/c4)^2)+a5*exp(-((x-556.308)/c5)^2)+a6*exp(-((x-572.872)/c6)^2)+a7*exp(-((x-594.385)/c7)^2)+a8*exp(-((x-603.98)/c8)^2)+a9*exp(-((x-618.726)/c9)^2)'

where I have hard-coded the peak locations. I use the following options:

opts.Lower = zeros(1,numel(pks));

opts.MaxFunEvals = 5000;

opts.MaxIter = 5000;

However, the resulting fit ends up being pretty bad:

Any ideas as to how I can do this robustly? I would like to be able to automate this for many other such signals. I eventually want to integrate over individual peaks.