Fourier Analysis Using the DFT Quote of the Day On two occasions I have been asked, “Pray, Mr. Babbage, if you put into the machine wrong figures, will.

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Presentation transcript:

Fourier Analysis Using the DFT Quote of the Day On two occasions I have been asked, “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. Charles Babbage Content and Figures are from Discrete-Time Signal Processing, 2e by Oppenheim, Shafer, and Buck, © Prentice Hall Inc.

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 2 Fourier Analysis of Signals Using DFT One major application of the DFT: analyze signals Let’s analyze frequency content of a continuous-time signal Steps to analyze signal with DFT –Remove high-frequencies to prevent aliasing after sampling –Sample signal to convert to discrete-time –Window to limit the duration of the signal –Take DFT of the resulting signal

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 3 Example Continuous- time signal Anti-aliasing filter Signal after filter

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 4 Example Cont’d Sampled discrete- time signal Frequency response of window Windowed and sampled Fourier transform

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 5 Effect of Windowing on Sinusoidal Signals The effects of anti-aliasing filtering and sampling is known We will analyze the effect of windowing Choose a simple signal to analyze this effect: sinusoids Assume ideal sampling and no aliasing we get And after windowing we have Calculate the DTFT of v[n] by writing out the cosines as

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 6 Example Consider a rectangular window w[n] of length 64 Assume 1/T=10 kHz, A 0 =1 and A 1 =0.75 and phases to be zero Magnitude of the DTFT of the window

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 7 Example Cont’d Magnitude of the DTFT of the sampled signal We expect to see dirac function at input frequencies Due to windowing we see, instead, the response of the window Note that both tones will affect each other due to the smearing –This is called leakage: pretty small in this example

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 8 Example Cont’d If we the input tones are close to each other On the left: the tones are so close that they have considerable affect on each others magnitude On the right: the tones are too close to even separate in this case –They cannot be resolved using this particular window

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 9 Window Functions Two factors are determined by the window function –Resolution: influenced mainly by the main lobe width –Leakage: relative amplitude of side lobes versus main lobe We know from filter design chapter that we can choose various windows to trade-off these two factors Example: –Kaiser window

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 10 The Effect of Spectral Sampling DFT samples the DTFT with N equally spaced samples at Or in terms of continuous-frequency Example: Signal after windowing

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 11 Example Cont’d Note the peak of the DTFTs are in between samples of the DFT DFT doesn’t necessary reflect real magnitude of spectral peaks

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 12 Example Cont’d Let’s consider another sequence after windowing we have

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 13 Example Cont’d In this case N samples cover exactly 4 and 8 periods of the tones The samples correspond to the peak of the lobes The magnitude of the peaks are accurate Note that we don’t see the side lobes in this case

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 14 Example: DFT Analysis with Kaiser Window The windowed signal is given as Where w K [n] is a Kaiser window with β=5.48 for a relative side lobe amplitude of -40 dB The windowed signal

Copyright (C) 2005 Güner Arslan 351M Digital Signal Processing 15 Example Cont’d DFT with this Kaiser window The two tones are clearly resolved with the Kaiser window