wienerfilter_plot.m

AR模型的源程序。

% File: WienerFilter_Plot.m
% -------------------------
% This file is used to draw the various plots

function WienerFilter_Plot()

load WienerFilter_SeqGen.mat;
load WienerFilter_Core.mat;
load WienerFiltering.mat;

figure();
% Plots I
% Plot the original signal s(n) and the input sequence x(n) for the last
% 100 samples
for i = 1: 100
    signalvector_s_cast(i) = signalvector_s(400 + i);
    signalvector_x_cast(i) = signalvector_x(400 + i);
end

index = [401: 1: L];
subplot(2, 2, 1);
hold all;
xlabel('400 \leq {\itn} \leq 500');
ylabel('Sample of Random Sequences');
title('Original Signal {\its(n)} vs. Input Sequence {\itx(n)}');

plot(index, signalvector_s_cast);
plot(index, signalvector_x_cast); 

legend( 'Original Signal', 'Input Sequence');
grid off;
hold off;

% Plots II
% Plot the ideal filter output and fir filter output for the last 100
% samples
for i = 1: 100
    output_vector_fir_cast(i) = output_fir(400 + i);
    output_vector_ide_cast(i) = output_ide(400 + i);
end

%index = [401: 1: L];
%subplot(2, 2, 2);
%hold all;
%xlabel('400 \leq {\itn} \leq 500');
%ylabel('Sample of Random Sequences');
%title('Ideal Filter Output{\its_I(n)} vs. FIR Filter Output{\its_R(n)}');

%plot(index, output_vector_ide_cast);
%plot(index, output_vector_fir_cast); 

%legend( 'Ideal Filter Output', 'FIR Filter Output');
%grid off;
%hold off;
% Plot the ideal wiener filter and the fir wiener filter for N samples
index = [1: 1: N];
subplot(2, 2, 2);
hold all;
xlabel('1 \leq {\itn} \leq N');
ylabel('Filter Samples');
title('Ideal Wiener Filter {\ith_I(n)} vs. FIR Wiener Filter {\ith_f(n)}');

plot(index, h_ide);
plot(index, h_fir); 

legend( 'Ideal Filter', 'FIR Filter');
grid off;
hold off;

% Plots III
% Plot the ideal filter output and the original signal for the last 100
% samples
index = [401: 1: L];
subplot(2, 2, 3);
hold all;
xlabel('400 \leq {\itn} \leq 500');
ylabel('Sample of Random Sequences');
title('Ideal Filter Output {\its_I(n)} vs. Original Signal {\its(n)}');

plot(index, signalvector_s_cast); 
plot(index, output_vector_ide_cast);

legend('Original Signal', 'Ideal Filter Output');
grid off;
hold off;

% Plots IV
% Plot the fir filter output and the original signal for the last 100
% samples
index = [401: 1: L];
subplot(2, 2, 4);
hold all;
xlabel('400 \leq {\itn} \leq 500');
ylabel('Sample of Random Sequences');
title('FIR Filter Output {\its_R(n)} vs. Original Signal {\its(n)}');

plot(index, signalvector_s_cast); 
plot(index, output_vector_fir_cast);

legend( 'Original Signal', 'FIR Filter Output');
grid off;
hold off;

clear;