dsp.FourthOrderSectionFilter

Filter a noisy sinusoidal signal using thedsp.FourthOrderSectionFilterobject. Visualize the original and filtered signals using a spectrum analyzer.

frameSize = 1024; fs = 1000; Sine1 = dsp.SineWave(5,100,'SamplesPerFrame',1024,'SampleRate',fs); Sine2 = dsp.SineWave(2,350,pi/2,'SamplesPerFrame',1024,...'SampleRate',fs); x = Sine1()+Sine2()+1e-4.*randn(Sine1.SamplesPerFrame,1);

%N = [2,4];%gain = [5,10];%centerFreq = [0.025,0.75];%bandwidth = [0.025,0.35];%mode = 'fos';%[num,den] = designParamEQ(N,gain,centerFreq,bandwidth,mode,'Orientation','row');num = [1.0223 -1.9368 0.9205 0 0 1.5171 2.3980 1.4317 0.6416 0.2752]; den = [-1.9368 0.9428 0 0 2.0136 1.9224 1.0260 0.3016];

fos = dsp.FourthOrderSectionFilter('Numerator',num,...'Denominator',den); scope = spectrumAnalyzer(...'SampleRate',fs,...'PlotAsTwoSidedSpectrum',false,...'FrequencyScale','linear',...'Method','welch',...'Title','Original and Filtered Signals',...'ShowLegend',true,...'ChannelNames',{'Original Signal','Filtered Signal'});

fori = 1:10000 y = fos(x); scope([x,y]);end释放(scope);

Design a lowpass fourth-order section (FOS) filter using thefdesignfunction. Using this filter, filter a noisy sinusoidal signal with two tones, one at 3 kHz, and the other at 12 kHz.

Design a fifth-order filter using the elliptic method in the'df2tsos'structure. Use L-infinity norm scaling in the frequency domain. Specify the passband frequency to be 0.15 $\pi$rad/sample and the stopband frequency to be 0.25 $\pi$rad /样品。指定1 dBllowable passband ripple and a stopband attenuation of 60 dB.

Fp = 0.15; Fst = 0.25; Ap = 1; Ast = 60;

The filter coefficients are scaled using anfdopts.sosscalingobject. The scaling object is defined to have no numerator constraints, and theScaleValueConstraintis set to'unit', specifying the scaling to be unity scaling.

fdo = fdopts.sosscaling; fdo.NumeratorConstraint='none'; fdo.ScaleValueConstraint='unit'; f = fdesign.lowpass('Fp,Fst,Ap,Ast',Fp,Fst,Ap,Ast); hFilter = design(f,'ellip','SystemObject',true,...'FilterStructure','df2tsos','SOSScaleNorm','Linf',...'SOSScaleOpts',fdo)

hFilter = dsp.SOSFilter with properties: Structure: 'Direct form II transposed' CoefficientSource: 'Property' Numerator: [3x3 double] Denominator: [3x3 double] HasScaleValues: true ScaleValues: [1 1 1 1.0000] Show all properties

Visualize the lowpass frequency response of the designed filter usingfvtool.

fvtool(hFilter)

有限公司nvert the lowpass filter to a bandpass filter using theiirlp2bpfunction.

[num,den] = iirlp2bp(hFilter.Numerator,hFilter.Denominator,Fp,[0.25,0.75])

num =3×50.2456 0 -0.2456 0 0 0.4175 -0.0000 -0.4206 -0.0000 0.4175 0.4281 -0.0000 -0.6433 -0.0000 0.4281

den =3×51.0000 -0.0000 0.5088 0 0 1.0000 -0.0000 0.0060 -0.0000 0.8657 1.0000 -0.0000 0.5160 -0.0000 0.5283

Create a fourth-order section filter using these numerator and denominator coefficients.

fos = dsp.FourthOrderSectionFilter(num,den)

fos = FourthOrderSectionFilter with properties: Numerator: [3x5 double] Denominator: [3x5 double]

Visualize the frequency response of the fourth-order section filter usingfvtool.

fvtool(fos);

Filter a noisy input signal with the fourth-order section filter. Visualize the spectra of the original signal and the filtered signal using the spectrum analyzer.

The input is a sum of two sine waves with frequencies 3 kHz and 12 kHz, respectively. The input sample rate is 44.1 kHz, and the frame size is set to 1024 samples.

fs = 44100; FrameLength = 1024; SINE1 = dsp.SineWave('SamplesPerFrame',FrameLength,'SampleRate',fs,'Frequency',3000); SINE2 = dsp.SineWave('SamplesPerFrame',FrameLength,'SampleRate',fs,'Frequency',12000);

Initialize a spectrum analyzer to visualize the signal spectra.

scope = spectrumAnalyzer(...'SampleRate',fs,...'PlotAsTwoSidedSpectrum',false,...'Title','Original and Filtered Signals',...'ShowLegend',true,...'YLimits',[-180 50],...'ChannelNames',{'Original Signal','Filtered Signal'});for指数= 1:1000 x = SINE1 SINE2 () + () + 0.001 * randn (FrameLength,1); y = fos(x); scope([x,y]);end