inttst3
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% % 1/5/94 Eric Jacobsen % % Interpolator comparison test simulator. % % This version compares the quadratic/parabolic and Quinn's % interpolator across a bin with fractionally spaced samples. % % Variables NZ and NOISE must be intialized. % NZ = 0/1 turns the noise generator off/on. NOISE, set % to 1.0, 0.7071, and 0.5, provides -3, 0, and +3dB SNR. % % % Last modified: % % 6/14/02 - eaj Added tests for Macleod's and Quinn's 2nd method. % 1/28/99 - eaj Cleaned up a little for readability. % tstlen=64; % Effective length of test vector. NZ=1; % Switch to enable (1) or disable (0) noise. NOISE=0.5; % Set noise level. ACW=1; % Amplitude of tone. N=10000; % Number of trials in test. %hw=Hanning(tstlen); % Hanning window for Grandke's method. % Requires Signal Processing toolbox. %bin=9.25; % Desired bin number of tone relative to long test length. %fid = fopen('intdata.doc','w'); % Open file for results. quinerr=zeros(size(1:N)); % Allocate vector for results. quaderr=zeros(size(1:N)); % Allocate vector for results. quin2err=zeros(size(1:N)); % Allocate vector for results. maclderr=zeros(size(1:N)); % Allocate vector for results. %jainerr=zeros(size(1:N)); % Allocate vector for results. %granerr=zeros(size(1:N)); % Allocate vector for results. quinest=zeros(size(1:N)); % Allocate vector for results. quadest=zeros(size(1:N)); % Allocate vector for results. quin2est=zeros(size(1:N)); % Allocate vector for results. macldest=zeros(size(1:N)); % Allocate vector for results. %jainest=zeros(size(1:N)); % Allocate vector for results. %granest=zeros(size(1:N)); % Allocate vector for results. SNR=zeros(size(1:N)); % Allocate vector for results. K=10; % Number of bins to test. quinr=zeros(size(1:K)); % Allocate vector for results. quadr=zeros(size(1:K)); % Allocate vector for results. quin2r=zeros(size(1:K)); % Allocate vector for results. macldr=zeros(size(1:K)); % Allocate vector for results. %jainr=zeros(size(1:K)); % Allocate vector for results. %granr=zeros(size(1:K)); % Allocate vector for results. quinvar=zeros(size(1:K)); % Allocate vector for results. quadvar=zeros(size(1:K)); % Allocate vector for results. quin2var=zeros(size(1:K)); % Allocate vector for results. macldvar=zeros(size(1:K)); % Allocate vector for results. %jainvar=zeros(size(1:K)); % Allocate vector for results. %granvar=zeros(size(1:K)); % Allocate vector for results. quinbias=zeros(size(1:K)); % Allocate vector for results. quadbias=zeros(size(1:K)); % Allocate vector for results. quin2bias=zeros(size(1:K)); % Allocate vector for results. macldbias=zeros(size(1:K)); % Allocate vector for results. %jainbias=zeros(size(1:K)); % Allocate vector for results. %granbias=zeros(size(1:K)); % Allocate vector for results. targ=zeros(size(1:K)); % Allocate vector for results. M=0; % Current test number. binstrt = 9.0; % Starting bin number. binstep = 0.1; % Bin delta step size. binend = 9.9; % Ending bin number. for bin = binstrt: binstep: binend, M=M+1; % Current test number. target=bin; % Calculate desired target result for comparison. targ(M)=bin; %fprintf(fid,'Target is %f.\n',target); %fprintf(fid,'N = %f.\n',N); fprintf('Peak is at %f.\n',bin); %for C = 1.0: -0.25: 0.25, % Run these tests for NZ. %NOISE=C; for I = 1:N, % Run trials. phz=2*pi*rand(1); cw=ACW*exp(j*((2*pi*(1:tstlen)*bin/tstlen)+phz)); % Generate signal. sigp=sum(abs(cw(1:tstlen).^2)); % Calculate signal power. cwn=cw; if(NZ==1) % Generate channel noise. % Set signal level and add noise (noise variance=1 on I and on Q) nzi=NOISE*randn(1,tstlen); nzq=NOISE*randn(1,tstlen); nzv=nzi+j*nzq; nzp=sum(abs(nzv(1:tstlen).^2)); if(nzp>0) SNR(I)=10*log10(sigp/nzp); % Calculate SNR=CNR. else SNR(I)=100.0; % Use this for no noise. end cwn=cw+nzv; end % End channel noise. dftshrt(1:tstlen)=fft(cwn(1:tstlen)); % DFT. magshrt(1:tstlen)=abs(dftshrt); % DFT magnitude. [rawmag,rawind]=max(magshrt); % Find raw peak magnitude and location. pk3vect(1:3)=dftshrt(rawind-1:rawind+1); % Isolated 3 samples around peak. quinest(I)=rawind-1+quin(pk3vect); % Do Quinn's first estimation. quinerr(I)=target-quinest(I); % Calculate and save error. quadest(I)=rawind-1+quadterp(pk3vect); % Do modified quadratic estimation. quaderr(I)=target-quadest(I); % Calculate and save error. quin2est(I)=rawind-1+quin2(pk3vect); % Do Quinn's second estimation. quin2err(I)=target-quin2est(I); % Calculate and save error. macldest(I)=rawind-1+macleod(pk3vect); % Do Macleod's estimation. maclderr(I)=target-macldest(I); % Calculate and save error. % The following is Jain's method. % if(pk3vect(1)>pk3vect(3)) % Find which adjacent bin is greatest. % alpha=abs(pk3vect(2))/abs(pk3vect(1)); % delta=alpha/(1+alpha); % jainest(I)=rawind-2+delta; % Jain's method. % else % alpha=abs(pk3vect(3))/abs(pk3vect(2)); % jainest(I)=rawind-1+delta; % end % jainerr(I)=target-jainest(I); % Calculate and save error. % The following is Grandke's method. % hcwn=cwn.*hw'; % Apply Hanning window to DFT input. % gdftshrt(1:tstlen)=fft(hcwn(1:tstlen)); % Weighted DFT. % gmagshrt(1:tstlen)=abs(gdftshrt); % Weighted DFT magnitude. % [grawmag,grawind]=max(gmagshrt); % Find raw peak magnitude and location. % gpk3vect(1:3)=gdftshrt(grawind-1:grawind+1); % Isolated 3 samples around peak. % if(gpk3vect(1)>gpk3vect(3)) % Find which adjacent bin is greatest. % alpha=abs(gpk3vect(2))/abs(gpk3vect(1)); % delta=(2*alpha-1)/(alpha+1); % grandkest(I)=grawind-2+delta; % Grandke's method. % else % alpha=abs(gpk3vect(3))/abs(gpk3vect(2)); % delta=(2*alpha-1)/(alpha+1); % grandkest(I)=grawind-1+delta; % end % granerr(I)=target-grandkest(I); % Calculate and save error. end % End inner for loop. quinvar(M)=sqrt(mean(quinerr.^2)); % Calculate result rms error. quadvar(M)=sqrt(mean(quaderr.^2)); % Calculate result rms error. quin2var(M)=sqrt(mean(quin2err.^2)); % Calculate result rms error. macldvar(M)=sqrt(mean(maclderr.^2)); % Calculate result rms error. %jainvar(M)=sqrt(mean(jainerr.^2)); % Calculate result rms error. %granvar(M)=sqrt(mean(granerr.^2)); % Calculate result rms error. SNRmn=mean(SNR); % Calculate result mean. quinbias(M)=mean(quinerr); % Calculate bias. quadbias(M)=mean(quaderr); % Calculate bias. quin2bias(M)=mean(quin2err); % Calculate bias. macldbias(M)=mean(maclderr); % Calculate bias. %jainbias(M)=mean(jainerr); % Calculate bias. %granbias(M)=mean(granerr); % Calculate bias. quinr(M)=mean(quinest); % Calculate average result. quadr(M)=mean(quadest); % Calculate average result. quin2r(M)=mean(quin2est); % Calculate average result. macldr(M)=mean(macldest); % Calculate average result. %jainr(M)=mean(jainest); % Calculate average result. %granr(M)=mean(grandkest); % Calculate average result. %end; % End outer for loop. end; % End bin loop. %fclose(fid); xs(1:M)=binstrt+(((1:M)-1)*binstep); % Generate scale for plot horizontal axis. figure(1); plot(xs(1:M),targ(1:M),'r-',xs(1:M),quadr(1:M),'mo',xs(1:M),quinr(1:M),'gs',xs(1:M),quin2r(1:M),'k+',xs(1:M),macldr(1:M),'bx'); title('Average Peak Location Estimates (bin) vs Bin Number'); figure(2); plot(xs(1:M),quadvar(1:M),'mo:',xs(1:M),quinvar(1:M),'gs:',xs(1:M),quin2var(1:M),'k+:',xs(1:M),macldvar(1:M),'bx:'); title('Estimator Variance vs Bin Number'); figure(3); plot(xs(1:M),quadbias(1:M),'mo:',xs(1:M),quinbias(1:M),'gs:',xs(1:M),quin2bias(1:M),'k+:',xs(1:M),macldbias(1:M),'bx:'); title('Estimator Bias vs Bin Number'); %end; %