Signal Conditioning

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Need Help related to Power Spectrum of two signal

Hello Sir, 

I have two signals of different frequencies but i want to take the FFT power spectrum of both signals at same graph . So for that case i take the FFT power spectrum of both signal indivisualy and after this build the array. so i got the one graph and all the components in same graph. Now

The problem is that in that graph i want to extract the magnitudes at each frequency, but when i extract the information from final graph it will be the " 1D array of Cluster of 3 Elements" so its information is saprate for these two signal. but i want to tale the magnitude at each frequency , how can i got this sir . Please Help me 

 

 

Agnivesh
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My first question:

You say you are combining the outputs from the two FFT's; how exactly are you doing this?

The first thing you have to watch out for is that your sampling frequency and number of samples for each FFT is identical. Then you "combine" the signals by making the signal array portion of the FFT's waveform output into a 2 dimensional array, as opposed to a single dimensional array. Put the single dimensional information for each of the FFT's into its own array  in the 2 dimensional array of the waveform cluster, then feed it to your graph. It will show each FFT as a separate trace on the graph.

I am doing this in an application with 8 simulaneous FFT's, so I know it works.

 

Clear as mud?,   give it a try, good luck.

 

Dave

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Sir it works thanks but there is one new problem is that wahen i am going to extract the magnitude for every frequency it is also works, but my question is that if my frequency is in floating point then how can i extract magnitude for that frequency. i mean i extracted the magnitude for every frequency component starting from 0 to 1...2..3...4.............so on (Frequency ) but if my frwquency id 2.3Hz so how can i do this.i am sending you my VI also 

Agnivesh
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The way to get more resolution in your X axis is to increase the number of samples in your data set. Not the sampling frequency, but the actual number of samples you take in each data set.

 

Have fun

 

Dave

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