data-acquisition.us

[Guest]

We are trying to identify different noise components in a signal which is in the time domain. There are at least two different processes which contribute to the noise in our target signal. We must identify one from the other and the severity of each one: (1) "brown" or "pink-type noise, which has a negative, "linear-like" behavior in the frequency domain; (2) "step and Discharge" type response (noise) in the time domain. Problem: the latter type of noise is a totally random occurrence. The relative magnitude of the (1) is also unknown. Can someone help us to identify and separate these two noise processes by looking at the combined output?

Background of our project: trying to identify, quantify and create a model for different kinds of noise seen in a 10-25 MHz signal. There are different processes at work creating noise in our system (thermal, external coupling, and most we are unfamiliar with).The noise created by different processes can be seen empirically but we have about a terabyte of data (millions of waveforms) which I need to analyze. We will create a computer program to look at each waveform and try and identify where different kinds of noise can be seen. When this is done the next step is to create a statistical model for each noise phenomenon.

[Stuart45]

The spectral energy density is useful where you are trying to quantify the processes involved and the sources are not correlated.