www.gusucode.com > dsp 案例源码程序 matlab代码 > dsp/UsingTheSpectrumAnalyzerSystemObjectExample.m
%% Estimate the PSD Using dsp.SpectrumAnalyzer
% To view the PSD of a signal, you can use the |dsp.SpectrumAnalyzer|
% System object(TM).
% You can change the dynamics of the input signal and see the effect
% those changes have on the spectral density of the signal in real time.
% The PSD data can only be viewed and is not available
% for processing. To acquire and process the data, use the
% |dsp.SpectrumEstimator| System object. For more information, see the
% section 'Estimate PSD Using dsp.SpectrumEstimator'.
%% Initialization
% Initialize the sine wave source to generate the sine wave and the
% spectrum analyer to show the PSD of the signal.
% The input sine wave has two frequencies: one at 1000 Hz and the other at
% 5000 Hz. Create two |dsp.SineWave| objects, one to generate the 1000 Hz
% sine wave and the other to generate the 5000 Hz sine wave.
Fs = 44100;
Sineobject1 = dsp.SineWave('SamplesPerFrame',1024,'PhaseOffset',10,...
'SampleRate',Fs,'Frequency',1000);
Sineobject2 = dsp.SineWave('SamplesPerFrame',1024,...
'SampleRate',Fs,'Frequency',5000);
SA = dsp.SpectrumAnalyzer('SampleRate',Fs,'SpectrumType','Power density',...
'PlotAsTwoSidedSpectrum',false,'Window','Hann',...
'ChannelNames',{'PSD of the input'},'ShowLegend',true);
%%
% The spectrum analyzer uses the |Hann| window to compute the power
% spectral density of the signal.
%% Estimation
% Stream in and estimate the PSD of the signal.
% Construct a |for|-loop to run for 5000 iterations. In each iteration,
% stream in 1024 samples (one frame) of each sine wave and compute the PSD of
% each frame.
% To generate the input signal, add the two sine waves. The resultant
% signal is a sine wave with two frequencies: one at 1000 Hz and the
% other at 5000 Hz. Add Gaussian noise with mean at 0 and a standard
% deviation of 0.001.
for Iter = 1:7000
Sinewave1 = Sineobject1();
Sinewave2 = Sineobject2();
Input = Sinewave1 + Sinewave2;
NoisyInput = Input + 0.001*randn(1024,1);
SA(NoisyInput);
end
%%
% In the spectrum analyzer output, you can see two distinct peaks: one at
% 1000 Hz and the other at 5000 Hz.
%%
% Resoultion Bandwidth (RBW) is the minimum frequency bandwidth that
% can be resolved by the spectrum analyzer. By default, the
% |RBWSource| property of the |dsp.SpectrumAnalyzer| object is set to |Auto|.
% In this mode, RBW is the ratio of the frequency span to 1024. In a
% two-sided spectrum, this value is $\frac{F_{s}}{1024}$, while in a one-sided
% spectrum, it is $\frac{\frac{F_{s}}{2}}{1024}$.
%
% Using this value of $RBW$, the window length($N_{samples}$) is computed
% iteratively using this relationship:
% $N_{samples} = \frac{(1-\frac{Overlap}{100})*(NENBW)*F_{s}}{RBW}$
%
% $Overlap$ is the amount of overlap between the previous and current
% buffered data segments. $NENBW$ is the equivalent noise bandwidth of the
% window. For more information on the details of the spectral estimation algorithm,
% see <matlab:web(fullfile(docroot,'dsp','ug','spectral-analysis.html'))
% Spectral Analysis>.
%
% $RBW$ calculated in this mode gives a good frequency resolution.
%
% To distinguish between two frequencies in the display, the distance
% between the two frequencies must be at least RBW.
% In this example, the distance between the two peaks is 4000 Hz, which is
% greater than $RBW$. Hence, you can see the peaks distinctly.
% Change the frequency of the second sine wave to 1015 Hz. The
% difference between the two frequencies is less than $RBW$.
release(Sineobject2);
Sineobject2.Frequency = 1015;
for Iter = 1:5000
Sinewave1 = Sineobject1();
Sinewave2 = Sineobject2();
Input = Sinewave1 + Sinewave2;
NoisyInput = Input + 0.001*randn(1024,1);
SA(NoisyInput);
end
%%
% The peaks are not distinguishable.
%
% <<../zoomedimage_15.png>>
%
%%
% To increase the frequency resolution, decrease $RBW$ to 1 Hz.
SA.RBWSource = 'property';
SA.RBW = 1;
for Iter = 1:5000
Sinewave1 = Sineobject1();
Sinewave2 = Sineobject2();
Input = Sinewave1 + Sinewave2;
NoisyInput = Input + 0.001*randn(1024,1);
SA(NoisyInput);
end
%%
% On zooming, the two peaks, which are 15 Hz apart, are now distinguishable.
%
% <<../zoomedimage_1.png>>
%%
% When you increase the frequency resolution, the window length increases,
% but the tradeoff is that the time resolution decreases.
%%
% During streaming, you can change the input properties or the spectrum
% analyzer properties and see the effect on the spectrum
% analyzer output immediately.
% For example, change the frequency of the second sine wave when the index
% of the loop is a multiple of 1000.
%
% To maintain a good balance between the frequency resolution and time
% resolution, change the |RBWSource| property to |Auto|.
release(Sineobject2);
SA.RBWSource = 'Auto';
for Iter = 1:5000
Sinewave1 = Sineobject1();
if (mod(Iter,1000) == 0)
release(Sineobject2);
Sineobject2.Frequency = Iter;
Sinewave2 = Sineobject2();
else
Sinewave2 = Sineobject2();
end
Input = Sinewave1 + Sinewave2;
NoisyInput = Input + 0.001*randn(1024,1);
SA(NoisyInput);
end
%%
% While running the streaming loop, you can see that the peak of the second
% sine wave changes according to the iteration value. Similarly, you can
% change any of the spectrum analyzer properties while the simulation is
% running and see a corresponding change in the output.