Is there an accepted measure of noise in a data set? I am taking a series of reading from an ADC, which follow a trend (they are not random data points). However, they generally lie above and below the averaged value (running average, FIR low pass effectively). How do I get a measure of how much noise there is over a given interval?
If you have the average value of the data set you are interested in and all you want to compute is standard deviation (or RMS because it is the same) then: -
- Subtract a sample value from the average
- Square that new value
- Sum all the squared values
- Divide the total by the number of samples
- Take the square root
If the noise is small and might contain quantization noise then you are going to be less accurate with the noise value computed.
If the mean/average is expected to drift in time then you may choose to use a rolling average calculation so that a significant emerging offset does not make the noise value bigger than it actually is.