I am trying to estimate measured signal, which has multimodal behaviour, usually 1-3 modes (see trimodal sample frequencies below for example), but in one experimental setup it's 1, 2 or 3 all the time.
First of all, I need to confirm this can be modelled as a mix of Gaussians (as some research from the problem domain hints, but I am slightly sceptical before I check my data). The underlying physics of the process is multi-path propagation of the signal, so different measurements may be from different paths (there are other effects, but this seems to be the strongest). My concern is there are not many different values for that (and sample size may also be limited to a thousands).
And more important, how to get the estimate of the mode with highest value (NB. they are negative).
My approach is to use weighted sum for the 5 highest values (by frequency) as it simple to calculate in the iterative manner (when needed in dynamic case).
Update: to the very least, will it be too wrong to just ignore measurements after second negative peak like -94 and try to fit those to normal distribution (or maybe Rayleigh)?