• date post

21-Feb-2016
• Category

## Documents

• view

215

1

Embed Size (px)

description

Spectrum Estimation

### Transcript of Spectrum Estimation

13

Department of Kinesiology and Applied PhysiologySpectrum EstimationW. Rose 2013-04-06Department of Kinesiology and Applied PhysiologySignal x(t) t=0 to TT=sampling interval=1/fsampN=number of samplesT=N TSimple spectrum estimateS(f) = power spectrum = k||X(f)||2 X(f)=discrete Fourier transform of x(t) (X(f) is complex)wheref = 0 to fN/2 = 0 to fsamp/2 (single-sided)f = 0 to f(N-1) (two-sided)and wheref=1/TDepartment of Kinesiology and Applied PhysiologySpectrum DetailsS(f) = power spectrum = k||X(f)||2 whereX(f) = discrete Fourier transform of x(t)X(f) is complex; S(f) is realIf x(t) has N pointsThen two-sided spectrum has N pointsone-sided spectrum has N/2+1 pointsk=normalizing factor; depends on particular routine used to calculate spectrum: single or double sided, peak or RMS units, etc. (1)Department of Kinesiology and Applied Physiology*dk=4/(k)=amplitudes for 1V square wave (k odd).Department of Kinesiology and Applied PhysiologyExample of the simple spectrum estimate

Matlab code to make t and x(t) and plot:

dt=0.01t=(0:499)*dt; f=1;d1=4/pi;d3=4/(3*pi);d5=4/(5*pi);x=d1*sin(2*pi*f*t) +d3*sin(2*pi*3*f*t) +d5*sin(2*pi*5*f*t);plot(t,x,.r-);Department of Kinesiology and Applied PhysiologyExample of the simple spectrum estimateMatlab code to compute amplitude spectrum using fft(x):X=fft(x);Xamp_2s=abs(X);N=length(x);T=N*dt;df=1/T;freqs=(0:N-1)*df;plot(freqs,Xamp_2s,.r-);

Note: fft(x) returns a 2-sided spectrum, as is evident from the graph below.Amplitude spectrum (two-sided)Department of Kinesiology and Applied PhysiologyExample of the simple spectrum estimateThe non-zero values |X(f)| = dkN/2, where dk=the amplitude of the sinusoid used to contruct x(t), as shown on an earlier slide. This shows that values returned by fft(x) scale like N/2, except at f=0 and f=fNyquist=fsamp/2, at which frequencies the values returned by fft(x) scale like N (not shown in this example). The divide by two scale factor is due to the fact that the energy that started out at 1 Hz is split between 1 Hz and 99 Hz in the two-sided spectrum.

Amplitude spectrum (two-sided)Department of Kinesiology and Applied PhysiologyExample of the simple spectrum estimateCompute the single-sided power spectrum. Divide by N at f=0 and f=fNyquist; divide by N/2 at all other frequencies.S=([Xamp_2s(1) Xamp_2s(2:N/2)*2 Xamp_2s(N/2+1)]/N).^2;freq1s=(0:N/2)*df; plot(freq1s,S,'.r-');xlabel('Frequency (Hz)'); ylabel('Power');This gives S(f)=d12,d32,d52 at the appropriate frequencies.

Single-sided power spectrumDepartment of Kinesiology and Applied PhysiologyA Better Spectrum EstimateOverview1. Divide the time domain record into blocks.2. Find power spectrum of each block.3. Average the power spectra, frequency by frequency.

Department of Kinesiology and Applied PhysiologyA Better Spectrum Estimate: DetailsDivide time domain record into segments of equal length. If there are q non-overlapping segments, then also include q-1 half-overlapped segments. Example: Total data record length=Ntot=4000 points. Investigator chooses q=4. Then each segment has length Nseg=Ntot/q=1000 points. The four non-overlapping blocks start at points 0, 1000, 2000, 3000. Three half-overlapped blocks start at 500, 1500, 2500. Total number of segments=2q-1=7.Department of Kinesiology and Applied PhysiologyA Better Spectrum Estimate: Details2. Find power spectrum of each segment.Before computing spectrum of each segment, remove linear trend, resulting in block with zero mean value and zero slope. Some prefer to remove only the mean value and not the linear trend (if any).Window the data in the segment with Hann or Hamming window.Compute power spectrum of windowed data. Correct power spectrum for the loss of power caused by the window:Hann window: multiply power estimates by 8/3. Hamming: multiply power estimates by 2.516.Department of Kinesiology and Applied PhysiologyA Better Spectrum Estimate: DetailsAverage the power spectra, frequency by frequency.Savg(f)=(1/(2q-1))*Sum(i=1 to 2q-1){Si(f)}where Si(f)=power at frequency f of the ith segment

Department of Kinesiology and Applied PhysiologyDepartment of Kinesiology and Applied PhysiologyScale Factors for Power Spectrum Estimatessingle-sided spectrum: At each non-zero frequency (from f to highest frequency below the halfway point, which is f=fsamp/2), multiply the two-sided estimate at that frequency by 2 to get the single-sided power estimate. The single sided spectrum only goes to fsamp/2. At f=0 and at f=fsamp/2, the single sided estimate is the same as the two-sided estimate.

See Labview help for Power Spectrum.vi (returns two-sided spectrum) and Auto Power Spectrum.vi. (returns one-sided spectrum).Department of Kinesiology and Applied PhysiologyDepartment of Kinesiology and Applied Physiology

Partial front panel of FFT_and_Power_Spectrum_Units.viExample VI in LV2012Output from FFT.vi scales like record length n. Other VIs return output whichh is independent of record length. FFT and Power_Spectrum return two-sided spectra, which is why their non DC scaling is divided by 2. The other VIs return one-sided spectra. FFT_Power_Spectral_Density divides each power spectrum estimate by f, the frequency spacing between estimates (f =spectral width of each estimate).