Peak detection of measured signal

Question

We use a data acquisition card to take readings from a device that increases its signal to a peak and then falls back to near the original value. To find the peak value we c

Answer 1

You could try signal averaging, i.e. for each point, average the value with the surrounding 3 or more points. If the noise blips are huge, then even this may not help.

I realise that this was language agnostic, but guessing that you are using LabView, there are lots of pre-packaged signal processing VIs that come with LabView that you can use to do smoothing and noise reduction. The NI forums are a great place to get more specialised help on this sort of thing.

Answer 2

Is there a qualitative difference between the desired peak and the unwanted second peak? If both peaks are "sharp" -- i.e. short in time duration -- when looking at the signal in the frequency domain (by doing FFT) you'll get energy at most bands. But if the "good" peak reliably has energy present at frequencies not existing in the "bad" peak, or vice versa, you may be able to automatically differentiate them that way.

Answer 3

You could apply some Standard Deviation to your logic and take notice of peaks over x%.

Answer 4

This problem has been studied in some detail.

There are a set of very up-to-date implementations in the TSpectrum* classes of ROOT (a nuclear/particle physics analysis tool). The code works in one- to three-dimensional data.

The ROOT source code is available, so you can grab this implementation if you want.

From the TSpectrum class documentation:

The algorithms used in this class have been published in the following references:

[1] M.Morhac et al.: Background elimination methods for multidimensional coincidence gamma-ray spectra. Nuclear Instruments and Methods in Physics Research A 401 (1997) 113- 132.

[2] M.Morhac et al.: Efficient one- and two-dimensional Gold deconvolution and its application to gamma-ray spectra decomposition. Nuclear Instruments and Methods in Physics Research A 401 (1997) 385-408.

[3] M.Morhac et al.: Identification of peaks in multidimensional coincidence gamma-ray spectra. Nuclear Instruments and Methods in Research Physics A 443(2000), 108-125.

The papers are linked from the class documentation for those of you who don't have a NIM online subscription.

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The short version of what is done is that the histogram flattened to eliminate noise, and then local maxima are detected by brute force in the flattened histogram.

Answer 5

I don't know very much about instrumentation, so this might be totally impractical, but then again it might be a helpful different direction. If you know how the readings can fail, and there is a certain interval between peaks given such failures, why not do gradient descent at each interval. If the descent brings you back to an area you've searched before, you can abandon it. Depending upon the shape of the sampled surface, this also might help you find peaks faster than search.

Answer 6

I think you want to cross-correlate your signal with an expected, exemplar signal. But, it has been such a long time since I studied signal processing and even then I didn't take much notice.