resonance peaks in frequency vs. amplitude spectrum

[lmtis]

Hello all,

I am struggling to find a way to analyze some data.  My data is frequency vs. amplitude spectrum plot.  There are resonance peaks in the spectrum.  When the data is clean I use peak detection to measure the frequency spacing of the peaks (the FSR).  When the peaks either have very low contrast, or are noisy, the peak detection doesn't work well.  I am thinking that I should be able to use one of the math functions like an FFT, but I haven't a clue as to which function to use or how.  Any help is greatly appreciated.

Jim

[DFGray]

Noisy data is always difficult to deal with, even for experts, but don't let that stop you.  Your analysis technique will vary depending upon how much information you have at the beginning.  However, trying to remove noise before analysis is somewhat dangerous.  The noise removal can often distort or remove data you want.  Instead, analyse the basic data with techniques which tend to "ignore" the noise.  Some examples are in order.

• If you know the approximate locations of your peaks, some sort of non-linear fit to a subset of the data for each peak would probably work.  This assumes you know the peak shape and can model it.  The noisier your data, the more robust your fit will need to be.  A Levenberg-Marquardt is fast and easy, but a downhill simplex is more reliable.
  
• If you know exactly what the peaks will look like, and their exact location when they are there, a singular-value decomposition is fast and efficient and fairly noise immune.
• If you have no clue where the peaks are, but have some idea of their width, try a Savitzky-Golay first derivative filter with a convolution width set by the known width of the peaks.  This will screen out the high frequency noise and give you a zero crossing for every peak and valley in your data.  You can tell which is which by the direction they cross zero.

Without some data, we can't help you much more.  Since you are new to this, I would highly recommend Numerical Recipes in C by Press et. al.  You will want the second edition (the first does not have Savitzky-Golay algorithms in it).  It gives a very clear explanation of most of the common numerical algorithms and why you would want to use them.  Most of the algorithms are already implemented in LabVIEW.  The building blocks are there for the rest (e.g. matrix multiply and invert, downhill simplex optimization).  Check your local university library.  Most have it.  I have even found it in municipal libraries.

Good luck.  Let us know if you have further questions.